Adam Lopez


2020

pdf bib
Inflecting When There’s No Majority: Limitations of Encoder-Decoder Neural Networks as Cognitive Models for German Plurals
Kate McCurdy | Sharon Goldwater | Adam Lopez
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Can artificial neural networks learn to represent inflectional morphology and generalize to new words as human speakers do? Kirov and Cotterell (2018) argue that the answer is yes: modern Encoder-Decoder (ED) architectures learn human-like behavior when inflecting English verbs, such as extending the regular past tense form /-(e)d/ to novel words. However, their work does not address the criticism raised by Marcus et al. (1995): that neural models may learn to extend not the regular, but the most frequent class — and thus fail on tasks like German number inflection, where infrequent suffixes like /-s/ can still be productively generalized. To investigate this question, we first collect a new dataset from German speakers (production and ratings of plural forms for novel nouns) that is designed to avoid sources of information unavailable to the ED model. The speaker data show high variability, and two suffixes evince ‘regular’ behavior, appearing more often with phonologically atypical inputs. Encoder-decoder models do generalize the most frequently produced plural class, but do not show human-like variability or ‘regular’ extension of these other plural markers. We conclude that modern neural models may still struggle with minority-class generalization.

pdf bib
LSTMs Compose—and Learn—Bottom-Up
Naomi Saphra | Adam Lopez
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent work in NLP shows that LSTM language models capture compositional structure in language data. In contrast to existing work, we consider the learning process that leads to compositional behavior. For a closer look at how an LSTM’s sequential representations are composed hierarchically, we present a related measure of Decompositional Interdependence (DI) between word meanings in an LSTM, based on their gate interactions. We support this measure with experiments on English language data, where DI is higher on pairs of words with lower syntactic distance. To explore the inductive biases that cause these compositional representations to arise during training, we conduct simple experiments on synthetic data. These synthetic experiments support a specific hypothesis about how hierarchical structures are discovered over the course of training: that LSTM constituent representations are learned bottom-up, relying on effective representations of their shorter children, rather than on learning the longer-range relations independently.

pdf bib
Conditioning, but on Which Distribution? Grammatical Gender in German Plural Inflection
Kate McCurdy | Adam Lopez | Sharon Goldwater
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Grammatical gender is a consistent and informative cue to the plural class of German nouns. We find that neural encoder-decoder models learn to rely on this cue to predict plural class, but adult speakers are relatively insensitive to it. This suggests that the neural models are not an effective cognitive model of German plural formation.

2019

pdf bib
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages
Clara Vania | Yova Kementchedjhieva | Anders Søgaard | Adam Lopez
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Parsers are available for only a handful of the world’s languages, since they require lots of training data. How far can we get with just a small amount of training data? We systematically compare a set of simple strategies for improving low-resource parsers: data augmentation, which has not been tested before; cross-lingual training; and transliteration. Experimenting on three typologically diverse low-resource languages—North Sámi, Galician, and Kazah—We find that (1) when only the low-resource treebank is available, data augmentation is very helpful; (2) when a related high-resource treebank is available, cross-lingual training is helpful and complements data augmentation; and (3) when the high-resource treebank uses a different writing system, transliteration into a shared orthographic spaces is also very helpful.

pdf bib
Semantic graph parsing with recurrent neural network DAG grammars
Federico Fancellu | Sorcha Gilroy | Adam Lopez | Mirella Lapata
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction. But predicting graphs presents difficult technical challenges, so it is simpler and more common to predict the *linearized* graphs found in semantic parsing datasets using well-understood sequence models. The cost of this simplicity is that the predicted strings may not be well-formed graphs. We present recurrent neural network DAG grammars, a graph-aware sequence model that generates only well-formed graphs while sidestepping many difficulties in graph prediction. We test our model on the Parallel Meaning Bank—a multilingual semantic graphbank. Our approach yields competitive results in English and establishes the first results for German, Italian and Dutch.

pdf bib
Pre-training on high-resource speech recognition improves low-resource speech-to-text translation
Sameer Bansal | Herman Kamper | Karen Livescu | Adam Lopez | Sharon Goldwater
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

We present a simple approach to improve direct speech-to-text translation (ST) when the source language is low-resource: we pre-train the model on a high-resource automatic speech recognition (ASR) task, and then fine-tune its parameters for ST. We demonstrate that our approach is effective by pre-training on 300 hours of English ASR data to improve Spanish English ST from 10.8 to 20.2 BLEU when only 20 hours of Spanish-English ST training data are available. Through an ablation study, we find that the pre-trained encoder (acoustic model) accounts for most of the improvement, despite the fact that the shared language in these tasks is the target language text, not the source language audio. Applying this insight, we show that pre-training on ASR helps ST even when the ASR language differs from both source and target ST languages: pre-training on French ASR also improves Spanish-English ST. Finally, we show that the approach improves performance on a true low-resource task: pre-training on a combination of English ASR and French ASR improves Mboshi-French ST, where only 4 hours of data are available, from 3.5 to 7.1 BLEU.

pdf bib
The problem with probabilistic DAG automata for semantic graphs
Ieva Vasiljeva | Sorcha Gilroy | Adam Lopez
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Semantic representations in the form of directed acyclic graphs (DAGs) have been introduced in recent years, and to model them, we need probabilistic models of DAGs. One model that has attracted some attention is the DAG automaton, but it has not been studied as a probabilistic model. We show that some DAG automata cannot be made into useful probabilistic models by the nearly universal strategy of assigning weights to transitions. The problem affects single-rooted, multi-rooted, and unbounded-degree variants of DAG automata, and appears to be pervasive. It does not affect planar variants, but these are problematic for other reasons.

pdf bib
Understanding Learning Dynamics Of Language Models with SVCCA
Naomi Saphra | Adam Lopez
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Research has shown that neural models implicitly encode linguistic features, but there has been no research showing how these encodings arise as the models are trained. We present the first study on the learning dynamics of neural language models, using a simple and flexible analysis method called Singular Vector Canonical Correlation Analysis (SVCCA), which enables us to compare learned representations across time and across models, without the need to evaluate directly on annotated data. We probe the evolution of syntactic, semantic, and topic representations, finding, for example, that part-of-speech is learned earlier than topic; that recurrent layers become more similar to those of a tagger during training; and embedding layers less similar. Our results and methods could inform better learning algorithms for NLP models, possibly to incorporate linguistic information more effectively.

2018

pdf bib
A Structured Syntax-Semantics Interface for English-AMR Alignment
Ida Szubert | Adam Lopez | Nathan Schneider
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Abstract Meaning Representation (AMR) annotations are often assumed to closely mirror dependency syntax, but AMR explicitly does not require this, and the assumption has never been tested. To test it, we devise an expressive framework to align AMR graphs to dependency graphs, which we use to annotate 200 AMRs. Our annotation explains how 97% of AMR edges are evoked by words or syntax. Previously existing AMR alignment frameworks did not allow for mapping AMR onto syntax, and as a consequence they explained at most 23%. While we find that there are indeed many cases where AMR annotations closely mirror syntax, there are also pervasive differences. We use our annotations to test a baseline AMR-to-syntax aligner, finding that this task is more difficult than AMR-to-string alignment; and to pinpoint errors in an AMR parser. We make our data and code freely available for further research on AMR parsing and generation, and the relationship of AMR to syntax.

pdf bib
What do character-level models learn about morphology? The case of dependency parsing
Clara Vania | Andreas Grivas | Adam Lopez
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

When parsing morphologically-rich languages with neural models, it is beneficial to model input at the character level, and it has been claimed that this is because character-level models learn morphology. We test these claims by comparing character-level models to an oracle with access to explicit morphological analysis on twelve languages with varying morphological typologies. Our results highlight many strengths of character-level models, but also show that they are poor at disambiguating some words, particularly in the face of case syncretism. We then demonstrate that explicitly modeling morphological case improves our best model, showing that character-level models can benefit from targeted forms of explicit morphological modeling.

bib
Graph Formalisms for Meaning Representations
Adam Lopez | Sorcha Gilroy
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

In this tutorial we will focus on Hyperedge Replacement Languages (HRL; Drewes et al. 1997), a context-free graph rewriting system. HRL are one of the most popular graph formalisms to be studied in NLP (Chiang et al., 2013; Peng et al., 2015; Bauer and Rambow, 2016). We will discuss HRL by formally defining them, studying several examples, discussing their properties, and providing exercises for the tutorial. While HRL have been used in NLP in the past, there is some speculation that they are more expressive than is necessary for graphs representing natural language (Drewes, 2017). Part of our own research has been exploring what restrictions of HRL could yield languages that are more useful for NLP and also those that have desirable properties for NLP models, such as being closed under intersection. With that in mind, we also plan to discuss Regular Graph Languages (RGL; Courcelle 1991), a subfamily of HRL which are closed under intersection. The definition of RGL is relatively simple after being introduced to HRL. We do not plan on discussing any proofs of why RGL are also a subfamily of MSOL, as described in Gilroy et al. (2017b). We will briefly mention the other formalisms shown in Figure 1 such as MSOL and DAGAL but this will focus on their properties rather than any formal definitions.

pdf bib
Weighted DAG Automata for Semantic Graphs
David Chiang | Frank Drewes | Daniel Gildea | Adam Lopez | Giorgio Satta
Computational Linguistics, Volume 44, Issue 1 - April 2018

Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning. A deficit in this area of research is a formal framework for creating, combining, and using models involving graphs that parallels the frameworks of finite automata for strings and finite tree automata for trees. A possible starting point for such a framework is the formalism of directed acyclic graph (DAG) automata, defined by Kamimura and Slutzki and extended by Quernheim and Knight. In this article, we study the latter in depth, demonstrating several new results, including a practical recognition algorithm that can be used for inference and learning with models defined on DAG automata. We also propose an extension to graphs with unbounded node degree and show that our results carry over to the extended formalism.

pdf bib
Does Ability Affect Alignment in Second Language Tutorial Dialogue?
Arabella Sinclair | Adam Lopez | C. G. Lucas | Dragan Gasevic
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

The role of alignment between interlocutors in second language learning is different to that in fluent conversational dialogue. Learners gain linguistic skill through increased alignment, yet the extent to which they can align will be constrained by their ability. Tutors may use alignment to teach and encourage the student, yet still must push the student and correct their errors, decreasing alignment. To understand how learner ability interacts with alignment, we measure the influence of ability on lexical priming, an indicator of alignment. We find that lexical priming in learner-tutor dialogues differs from that in conversational and task-based dialogues, and we find evidence that alignment increases with ability and with word complexity.

pdf bib
‘Indicatements’ that character language models learn English morpho-syntactic units and regularities
Yova Kementchedjhieva | Adam Lopez
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Character language models have access to surface morphological patterns, but it is not clear whether or how they learn abstract morphological regularities. We instrument a character language model with several probes, finding that it can develop a specific unit to identify word boundaries and, by extension, morpheme boundaries, which allows it to capture linguistic properties and regularities of these units. Our language model proves surprisingly good at identifying the selectional restrictions of English derivational morphemes, a task that requires both morphological and syntactic awareness. Thus we conclude that, when morphemes overlap extensively with the words of a language, a character language model can perform morphological abstraction.

pdf bib
Language Models Learn POS First
Naomi Saphra | Adam Lopez
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

A glut of recent research shows that language models capture linguistic structure. Such work answers the question of whether a model represents linguistic structure. But how and when are these structures acquired? Rather than treating the training process itself as a black box, we investigate how representations of linguistic structure are learned over time. In particular, we demonstrate that different aspects of linguistic structure are learned at different rates, with part of speech tagging acquired early and global topic information learned continuously.

pdf bib
Explicitly modeling case improves neural dependency parsing
Clara Vania | Adam Lopez
Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP

Neural dependency parsing models that compose word representations from characters can presumably exploit morphosyntax when making attachment decisions. How much do they know about morphology? We investigate how well they handle morphological case, which is important for parsing. Our experiments on Czech, German and Russian suggest that adding explicit morphological case—either oracle or predicted—improves neural dependency parsing, indicating that the learned representations in these models do not fully encode the morphological knowledge that they need, and can still benefit from targeted forms of explicit linguistic modeling.

2017

pdf bib
From Characters to Words to in Between: Do We Capture Morphology?
Clara Vania | Adam Lopez
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Words can be represented by composing the representations of subword units such as word segments, characters, and/or character n-grams. While such representations are effective and may capture the morphological regularities of words, they have not been systematically compared, and it is not understood how they interact with different morphological typologies. On a language modeling task, we present experiments that systematically vary (1) the basic unit of representation, (2) the composition of these representations, and (3) the morphological typology of the language modeled. Our results extend previous findings that character representations are effective across typologies, and we find that a previously unstudied combination of character trigram representations composed with bi-LSTMs outperforms most others. But we also find room for improvement: none of the character-level models match the predictive accuracy of a model with access to true morphological analyses, even when learned from an order of magnitude more data.

pdf bib
A Generative Parser with a Discriminative Recognition Algorithm
Jianpeng Cheng | Adam Lopez | Mirella Lapata
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Generative models defining joint distributions over parse trees and sentences are useful for parsing and language modeling, but impose restrictions on the scope of features and are often outperformed by discriminative models. We propose a framework for parsing and language modeling which marries a generative model with a discriminative recognition model in an encoder-decoder setting. We provide interpretations of the framework based on expectation maximization and variational inference, and show that it enables parsing and language modeling within a single implementation. On the English Penn Treen-bank, our framework obtains competitive performance on constituency parsing while matching the state-of-the-art single-model language modeling score.

pdf bib
UParse: the Edinburgh system for the CoNLL 2017 UD shared task
Clara Vania | Xingxing Zhang | Adam Lopez
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for low-resource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our system achieves rank 9th in the main results, with 75.49 UAS and 68.87 LAS F-1 scores (average across 81 treebanks).

pdf bib
Detecting negation scope is easy, except when it isn’t
Federico Fancellu | Adam Lopez | Bonnie Webber | Hangfeng He
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

Several corpora have been annotated with negation scope—the set of words whose meaning is negated by a cue like the word “not”—leading to the development of classifiers that detect negation scope with high accuracy. We show that for nearly all of these corpora, this high accuracy can be attributed to a single fact: they frequently annotate negation scope as a single span of text delimited by punctuation. For negation scopes not of this form, detection accuracy is low and under-sampling the easy training examples does not substantially improve accuracy. We demonstrate that this is partly an artifact of annotation guidelines, and we argue that future negation scope annotation efforts should focus on these more difficult cases.

pdf bib
Towards speech-to-text translation without speech recognition
Sameer Bansal | Herman Kamper | Adam Lopez | Sharon Goldwater
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers

We explore the problem of translating speech to text in low-resource scenarios where neither automatic speech recognition (ASR) nor machine translation (MT) are available, but we have training data in the form of audio paired with text translations. We present the first system for this problem applied to a realistic multi-speaker dataset, the CALLHOME Spanish-English speech translation corpus. Our approach uses unsupervised term discovery (UTD) to cluster repeated patterns in the audio, creating a pseudotext, which we pair with translations to create a parallel text and train a simple bag-of-words MT model. We identify the challenges faced by the system, finding that the difficulty of cross-speaker UTD results in low recall, but that our system is still able to correctly translate some content words in test data.

pdf bib
Parsing Graphs with Regular Graph Grammars
Sorcha Gilroy | Adam Lopez | Sebastian Maneth
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

Recently, several datasets have become available which represent natural language phenomena as graphs. Hyperedge Replacement Languages (HRL) have been the focus of much attention as a formalism to represent the graphs in these datasets. Chiang et al. (2013) prove that HRL graphs can be parsed in polynomial time with respect to the size of the input graph. We believe that HRL are more expressive than is necessary to represent semantic graphs and we propose the use of Regular Graph Languages (RGL; Courcelle 1991), which is a subfamily of HRL, as a possible alternative. We provide a top-down parsing algorithm for RGL that runs in time linear in the size of the input graph.

pdf bib
Universal Dependencies to Logical Form with Negation Scope
Federico Fancellu | Siva Reddy | Adam Lopez | Bonnie Webber
Proceedings of the Workshop Computational Semantics Beyond Events and Roles

Many language technology applications would benefit from the ability to represent negation and its scope on top of widely-used linguistic resources. In this paper, we investigate the possibility of obtaining a first-order logic representation with negation scope marked using Universal Dependencies. To do so, we enhance UDepLambda, a framework that converts dependency graphs to logical forms. The resulting UDepLambda¬ is able to handle phenomena related to scope by means of an higher-order type theory, relevant not only to negation but also to universal quantification and other complex semantic phenomena. The initial conversion we did for English is promising, in that one can represent the scope of negation also in the presence of more complex phenomena such as universal quantifiers.

pdf bib
(Re)introducing Regular Graph Languages
Sorcha Gilroy | Adam Lopez | Sebastian Maneth | Pijus Simonaitis
Proceedings of the 15th Meeting on the Mathematics of Language

pdf bib
Spoken Term Discovery for Language Documentation using Translations
Antonios Anastasopoulos | Sameer Bansal | David Chiang | Sharon Goldwater | Adam Lopez
Proceedings of the Workshop on Speech-Centric Natural Language Processing

Vast amounts of speech data collected for language documentation and research remain untranscribed and unsearchable, but often a small amount of speech may have text translations available. We present a method for partially labeling additional speech with translations in this scenario. We modify an unsupervised speech-to-translation alignment model and obtain prototype speech segments that match the translation words, which are in turn used to discover terms in the unlabelled data. We evaluate our method on a Spanish-English speech translation corpus and on two corpora of endangered languages, Arapaho and Ainu, demonstrating its appropriateness and applicability in an actual very-low-resource scenario.

2016

pdf bib
Neural Networks For Negation Scope Detection
Federico Fancellu | Adam Lopez | Bonnie Webber
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
N-gram language models for massively parallel devices
Nikolay Bogoychev | Adam Lopez
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Evaluating Informal-Domain Word Representations With UrbanDictionary
Naomi Saphra | Adam Lopez
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

2015

pdf bib
A Maximum Entropy Classifier for Cross-Lingual Pronoun Prediction
Dominikus Wetzel | Adam Lopez | Bonnie Webber
Proceedings of the Second Workshop on Discourse in Machine Translation

pdf bib
AMRICA: an AMR Inspector for Cross-language Alignments
Naomi Saphra | Adam Lopez
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations

pdf bib
Gappy Pattern Matching on GPUs for On-Demand Extraction of Hierarchical Translation Grammars
Hua He | Jimmy Lin | Adam Lopez
Transactions of the Association for Computational Linguistics, Volume 3

Grammars for machine translation can be materialized on demand by finding source phrases in an indexed parallel corpus and extracting their translations. This approach is limited in practical applications by the computational expense of online lookup and extraction. For phrase-based models, recent work has shown that on-demand grammar extraction can be greatly accelerated by parallelization on general purpose graphics processing units (GPUs), but these algorithms do not work for hierarchical models, which require matching patterns that contain gaps. We address this limitation by presenting a novel GPU algorithm for on-demand hierarchical grammar extraction that is at least an order of magnitude faster than a comparable CPU algorithm when processing large batches of sentences. In terms of end-to-end translation, with decoding on the CPU, we increase throughput by roughly two thirds on a standard MT evaluation dataset. The GPU necessary to achieve these improvements increases the cost of a server by about a third. We believe that GPU-based extraction of hierarchical grammars is an attractive proposition, particularly for MT applications that demand high throughput.

2013

pdf bib
Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT
Adam Lopez | Matt Post | Chris Callison-Burch | Jonathan Weese | Juri Ganitkevitch | Narges Ahmidi | Olivia Buzek | Leah Hanson | Beenish Jamil | Matthias Lee | Ya-Ting Lin | Henry Pao | Fatima Rivera | Leili Shahriyari | Debu Sinha | Adam Teichert | Stephen Wampler | Michael Weinberger | Daguang Xu | Lin Yang | Shang Zhao
Transactions of the Association for Computational Linguistics, Volume 1

Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-of-the-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.

pdf bib
Dirt Cheap Web-Scale Parallel Text from the Common Crawl
Jason R. Smith | Herve Saint-Amand | Magdalena Plamada | Philipp Koehn | Chris Callison-Burch | Adam Lopez
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

pdf bib
Massively Parallel Suffix Array Queries and On-Demand Phrase Extraction for Statistical Machine Translation Using GPUs
Hua He | Jimmy Lin | Adam Lopez
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

pdf bib
Putting Human Assessments of Machine Translation Systems in Order
Adam Lopez
Proceedings of the Seventh Workshop on Statistical Machine Translation

pdf bib
Using Categorial Grammar to Label Translation Rules
Jonathan Weese | Chris Callison-Burch | Adam Lopez
Proceedings of the Seventh Workshop on Statistical Machine Translation

2011

pdf bib
Training a Log-Linear Parser with Loss Functions via Softmax-Margin
Michael Auli | Adam Lopez
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

pdf bib
Joshua 3.0: Syntax-based Machine Translation with the Thrax Grammar Extractor
Jonathan Weese | Juri Ganitkevitch | Chris Callison-Burch | Matt Post | Adam Lopez
Proceedings of the Sixth Workshop on Statistical Machine Translation

pdf bib
A Comparison of Loopy Belief Propagation and Dual Decomposition for Integrated CCG Supertagging and Parsing
Michael Auli | Adam Lopez
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Efficient CCG Parsing: A* versus Adaptive Supertagging
Michael Auli | Adam Lopez
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

pdf bib
Proceedings of the 4th Workshop on Cross Lingual Information Access
Sudeshna Sarkar | Min Zhang | Adam Lopez | Raghavendra Udupa
Proceedings of the 4th Workshop on Cross Lingual Information Access

pdf bib
cdec: A Decoder, Alignment, and Learning Framework for Finite-State and Context-Free Translation Models
Chris Dyer | Adam Lopez | Juri Ganitkevitch | Jonathan Weese | Ferhan Ture | Phil Blunsom | Hendra Setiawan | Vladimir Eidelman | Philip Resnik
Proceedings of the ACL 2010 System Demonstrations

2009

pdf bib
A Systematic Analysis of Translation Model Search Spaces
Michael Auli | Adam Lopez | Hieu Hoang | Philipp Koehn
Proceedings of the Fourth Workshop on Statistical Machine Translation

pdf bib
Monte Carlo inference and maximization for phrase-based translation
Abhishek Arun | Chris Dyer | Barry Haddow | Phil Blunsom | Adam Lopez | Philipp Koehn
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

pdf bib
Translation as Weighted Deduction
Adam Lopez
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

2008

pdf bib
Tera-Scale Translation Models via Pattern Matching
Adam Lopez
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

pdf bib
Hierarchical Phrase-Based Translation with Suffix Arrays
Adam Lopez
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2005

pdf bib
The Hiero Machine Translation System: Extensions, Evaluation, and Analysis
David Chiang | Adam Lopez | Nitin Madnani | Christof Monz | Philip Resnik | Michael Subotin
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

pdf bib
Pattern Visualization for Machine Translation Output
Adam Lopez | Philip Resnik
Proceedings of HLT/EMNLP 2005 Interactive Demonstrations

pdf bib
Improved HMM Alignment Models for Languages with Scarce Resources
Adam Lopez | Philip Resnik
Proceedings of the ACL Workshop on Building and Using Parallel Texts