Christian Hardmeier


2020

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Exploiting Cross-Lingual Hints to Discover Event Pronouns
Sharid Loáiciga | Christian Hardmeier | Asad Sayeed
Proceedings of the 12th Language Resources and Evaluation Conference

Non-nominal co-reference is much less studied than nominal coreference, partly because of the lack of annotated corpora. We explore the possibility to exploit parallel multilingual corpora as a means of cheap supervision for the classification of three different readings of the English pronoun ‘it’: entity, event or pleonastic, from their translation in several languages. We found that the ‘event’ reading is not very frequent, but can be easily predicted provided that the construction used to translate the ‘it’ example is a pronoun as well. These cases, nevertheless, are not enough to generalize to other types of non-nominal reference.

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Proceedings of the First Workshop on Computational Approaches to Discourse
Chloé Braud | Christian Hardmeier | Junyi Jessy Li | Annie Louis | Michael Strube
Proceedings of the First Workshop on Computational Approaches to Discourse

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Coreference Strategies in English-German Translation
Ekaterina Lapshinova-Koltunski | Marie-Pauline Krielke | Christian Hardmeier
Proceedings of the Third Workshop on Computational Models of Reference, Anaphora and Coreference

We present a study focusing on variation of coreferential devices in English original TED talks and news texts and their German translations. Using exploratory techniques we contemplate a diverse set of coreference devices as features which we assume indicate language-specific and register-based variation as well as potential translation strategies. Our findings reflect differences on both dimensions with stronger variation along the lines of register than between languages. By exposing interactions between text type and cross-linguistic variation, they can also inform multilingual NLP applications, especially machine translation.

2019

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Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)
Andrei Popescu-Belis | Sharid Loáiciga | Christian Hardmeier | Deyi Xiong
Proceedings of the Fourth Workshop on Discourse in Machine Translation (DiscoMT 2019)

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Entity Decisions in Neural Language Modelling: Approaches and Problems
Jenny Kunz | Christian Hardmeier
Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

We explore different approaches to explicit entity modelling in language models (LM). We independently replicate two existing models in a controlled setup, introduce a simplified variant of one of the models and analyze their performance in direct comparison. Our results suggest that today’s models are limited as several stochastic variables make learning difficult. We show that the most challenging point in the systems is the decision if the next token is an entity token. The low precision and recall for this variable will lead to severe cascading errors. Our own simplified approach dispenses with the need for latent variables and improves the performance in the entity yes/no decision. A standard well-tuned baseline RNN-LM with a larger number of hidden units outperforms all entity-enabled LMs in terms of perplexity.

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Cross-lingual Incongruences in the Annotation of Coreference
Ekaterina Lapshinova-Koltunski | Sharid Loáiciga | Christian Hardmeier | Pauline Krielke
Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference

In the present paper, we deal with incongruences in English-German multilingual coreference annotation and present automated methods to discover them. More specifically, we automatically detect full coreference chains in parallel texts and analyse discrepancies in their annotations. In doing so, we wish to find out whether the discrepancies rather derive from language typological constraints, from the translation or the actual annotation process. The results of our study contribute to the referential analysis of similarities and differences across languages and support evaluation of cross-lingual coreference annotation. They are also useful for cross-lingual coreference resolution systems and contrastive linguistic studies.

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Proceedings of the First Workshop on Gender Bias in Natural Language Processing
Marta R. Costa-jussà | Christian Hardmeier | Will Radford | Kellie Webster
Proceedings of the First Workshop on Gender Bias in Natural Language Processing

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Gendered Ambiguous Pronoun (GAP) Shared Task at the Gender Bias in NLP Workshop 2019
Kellie Webster | Marta R. Costa-jussà | Christian Hardmeier | Will Radford
Proceedings of the First Workshop on Gender Bias in Natural Language Processing

The 1st ACL workshop on Gender Bias in Natural Language Processing included a shared task on gendered ambiguous pronoun (GAP) resolution. This task was based on the coreference challenge defined in Webster et al. (2018), designed to benchmark the ability of systems to resolve pronouns in real-world contexts in a gender-fair way. 263 teams competed via a Kaggle competition, with the winning system achieving logloss of 0.13667 and near gender parity. We review the approaches of eleven systems with accepted description papers, noting their effective use of BERT (Devlin et al., 2018), both via fine-tuning and for feature extraction, as well as ensembling.

2018

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ParCorFull: a Parallel Corpus Annotated with Full Coreference
Ekaterina Lapshinova-Koltunski | Christian Hardmeier | Pauline Krielke
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

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Getting Gender Right in Neural Machine Translation
Eva Vanmassenhove | Christian Hardmeier | Andy Way
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Speakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such difference is related to the way gender is expressed in a language. Saying “I am happy” in English, does not encode any additional knowledge of the speaker that uttered the sentence. However, many other languages do have grammatical gender systems and so such knowledge would be encoded. In order to correctly translate such a sentence into, say, French, the inherent gender information needs to be retained/recovered. The same sentence would become either “Je suis heureux”, for a male speaker or “Je suis heureuse” for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.) also influence our use of language in terms of word choices or syntactic constructions (Tannen, 1991; Pennebaker et al., 2003). We integrate gender information into NMT systems. Our contribution is two-fold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that adding a gender feature to an NMT system significantly improves the translation quality for some language pairs.

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Automatic Reference-Based Evaluation of Pronoun Translation Misses the Point
Liane Guillou | Christian Hardmeier
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We compare the performance of the APT and AutoPRF metrics for pronoun translation against a manually annotated dataset comprising human judgements as to the correctness of translations of the PROTEST test suite. Although there is some correlation with the human judgements, a range of issues limit the performance of the automated metrics. Instead, we recommend the use of semi-automatic metrics and test suites in place of fully automatic metrics.

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Universal Word Segmentation: Implementation and Interpretation
Yan Shao | Christian Hardmeier | Joakim Nivre
Transactions of the Association for Computational Linguistics, Volume 6

Word segmentation is a low-level NLP task that is non-trivial for a considerable number of languages. In this paper, we present a sequence tagging framework and apply it to word segmentation for a wide range of languages with different writing systems and typological characteristics. Additionally, we investigate the correlations between various typological factors and word segmentation accuracy. The experimental results indicate that segmentation accuracy is positively related to word boundary markers and negatively to the number of unique non-segmental terms. Based on the analysis, we design a small set of language-specific settings and extensively evaluate the segmentation system on the Universal Dependencies datasets. Our model obtains state-of-the-art accuracies on all the UD languages. It performs substantially better on languages that are non-trivial to segment, such as Chinese, Japanese, Arabic and Hebrew, when compared to previous work.

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Event versus entity co-reference: Effects of context and form of referring expression
Sharid Loáiciga | Luca Bevacqua | Hannah Rohde | Christian Hardmeier
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

Anaphora resolution systems require both an enumeration of possible candidate antecedents and an identification process of the antecedent. This paper focuses on (i) the impact of the form of referring expression on entity-vs-event preferences and (ii) how properties of the passage interact with referential form. Two crowd-sourced story-continuation experiments were conducted, using constructed and naturally-occurring passages, to see how participants interpret It and This pronouns following a context sentence that makes available event and entity referents. Our participants show a strong, but not categorical, bias to use This to refer to events and It to refer to entities. However, these preferences vary with passage characteristics such as verb class (a proxy in our constructed examples for the number of explicit and implicit entities) and more subtle author intentions regarding subsequent re-mention (the original event-vs-entity re-mention of our corpus items).

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Forms of Anaphoric Reference to Organisational Named Entities: Hoping to widen appeal, they diversified
Christian Hardmeier | Luca Bevacqua | Sharid Loáiciga | Hannah Rohde
Proceedings of the Seventh Named Entities Workshop

Proper names of organisations are a special case of collective nouns. Their meaning can be conceptualised as a collective unit or as a plurality of persons, allowing for different morphological marking of coreferent anaphoric pronouns. This paper explores the variability of references to organisation names with 1) a corpus analysis and 2) two crowd-sourced story continuation experiments. The first shows that the preference for singular vs. plural conceptualisation is dependent on the level of formality of a text. In the second, we observe a strong preference for the plural they otherwise typical of informal speech. Using edited corpus data instead of constructed sentences as stimuli reduces this preference.

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Discourse-Related Language Contrasts in English-Croatian Human and Machine Translation
Margita Šoštarić | Christian Hardmeier | Sara Stymne
Proceedings of the Third Conference on Machine Translation: Research Papers

We present an analysis of a number of coreference phenomena in English-Croatian human and machine translations. The aim is to shed light on the differences in the way these structurally different languages make use of discourse information and provide insights for discourse-aware machine translation system development. The phenomena are automatically identified in parallel data using annotation produced by parsers and word alignment tools, enabling us to pinpoint patterns of interest in both languages. We make the analysis more fine-grained by including three corpora pertaining to three different registers. In a second step, we create a test set with the challenging linguistic constructions and use it to evaluate the performance of three MT systems. We show that both SMT and NMT systems struggle with handling these discourse phenomena, even though NMT tends to perform somewhat better than SMT. By providing an overview of patterns frequently occurring in actual language use, as well as by pointing out the weaknesses of current MT systems that commonly mistranslate them, we hope to contribute to the effort of resolving the issue of discourse phenomena in MT applications.

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A Pronoun Test Suite Evaluation of the English–German MT Systems at WMT 2018
Liane Guillou | Christian Hardmeier | Ekaterina Lapshinova-Koltunski | Sharid Loáiciga
Proceedings of the Third Conference on Machine Translation: Shared Task Papers

We evaluate the output of 16 English-to-German MT systems with respect to the translation of pronouns in the context of the WMT 2018 competition. We work with a test suite specifically designed to assess system quality in various fine-grained categories known to be problematic. The main evaluation scores come from a semi-automatic process, combining automatic reference matching with extensive manual annotation of uncertain cases. We find that current NMT systems are good at translating pronouns with intra-sentential reference, but the inter-sentential cases remain difficult. NMT systems are also good at the translation of event pronouns, unlike systems from the phrase-based SMT paradigm. No single system performs best at translating all types of anaphoric pronouns, suggesting unexplained random effects influencing the translation of pronouns with NMT.

2017

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Character-based Joint Segmentation and POS Tagging for Chinese using Bidirectional RNN-CRF
Yan Shao | Christian Hardmeier | Jörg Tiedemann | Joakim Nivre
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We present a character-based model for joint segmentation and POS tagging for Chinese. The bidirectional RNN-CRF architecture for general sequence tagging is adapted and applied with novel vector representations of Chinese characters that capture rich contextual information and lower-than-character level features. The proposed model is extensively evaluated and compared with a state-of-the-art tagger respectively on CTB5, CTB9 and UD Chinese. The experimental results indicate that our model is accurate and robust across datasets in different sizes, genres and annotation schemes. We obtain state-of-the-art performance on CTB5, achieving 94.38 F1-score for joint segmentation and POS tagging.

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Recall is the Proper Evaluation Metric for Word Segmentation
Yan Shao | Christian Hardmeier | Joakim Nivre
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We extensively analyse the correlations and drawbacks of conventionally employed evaluation metrics for word segmentation. Unlike in standard information retrieval, precision favours under-splitting systems and therefore can be misleading in word segmentation. Overall, based on both theoretical and experimental analysis, we propose that precision should be excluded from the standard evaluation metrics and that the evaluation score obtained by using only recall is sufficient and better correlated with the performance of word segmentation systems.

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Findings of the 2017 DiscoMT Shared Task on Cross-lingual Pronoun Prediction
Sharid Loáiciga | Sara Stymne | Preslav Nakov | Christian Hardmeier | Jörg Tiedemann | Mauro Cettolo | Yannick Versley
Proceedings of the Third Workshop on Discourse in Machine Translation

We describe the design, the setup, and the evaluation results of the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The task asked participants to predict a target-language pronoun given a source-language pronoun in the context of a sentence. We further provided a lemmatized target-language human-authored translation of the source sentence, and automatic word alignments between the source sentence words and the target-language lemmata. The aim of the task was to predict, for each target-language pronoun placeholder, the word that should replace it from a small, closed set of classes, using any type of information that can be extracted from the entire document. We offered four subtasks, each for a different language pair and translation direction: English-to-French, English-to-German, German-to-English, and Spanish-to-English. Five teams participated in the shared task, making submissions for all language pairs. The evaluation results show that most participating teams outperformed two strong n-gram-based language model-based baseline systems by a sizable margin.

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Predicting Pronouns with a Convolutional Network and an N-gram Model
Christian Hardmeier
Proceedings of the Third Workshop on Discourse in Machine Translation

This paper describes the UU-Hardmeier system submitted to the DiscoMT 2017 shared task on cross-lingual pronoun prediction. The system is an ensemble of convolutional neural networks combined with a source-aware n-gram language model.

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Discovery of Discourse-Related Language Contrasts through Alignment Discrepancies in English-German Translation
Ekaterina Lapshinova-Koltunski | Christian Hardmeier
Proceedings of the Third Workshop on Discourse in Machine Translation

In this paper, we analyse alignment discrepancies for discourse structures in English-German parallel data – sentence pairs, in which discourse structures in target or source texts have no alignment in the corresponding parallel sentences. The discourse-related structures are designed in form of linguistic patterns based on the information delivered by automatic part-of-speech and dependency annotation. In addition to alignment errors (existing structures left unaligned), these alignment discrepancies can be caused by language contrasts or through the phenomena of explicitation and implicitation in the translation process. We propose a new approach including new type of resources for corpus-based language contrast analysis and apply it to study and classify the contrasts found in our English-German parallel corpus. As unaligned discourse structures may also result in the loss of discourse information in the MT training data, we hope to deliver information in support of discourse-aware machine translation (MT).

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What is it? Disambiguating the different readings of the pronoun ‘it’
Sharid Loáiciga | Liane Guillou | Christian Hardmeier
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In this paper, we address the problem of predicting one of three functions for the English pronoun ‘it’: anaphoric, event reference or pleonastic. This disambiguation is valuable in the context of machine translation and coreference resolution. We present experiments using a MAXENT classifier trained on gold-standard data and self-training experiments of an RNN trained on silver-standard data, annotated using the MAXENT classifier. Lastly, we report on an analysis of the strengths of these two models.

2016

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PROTEST: A Test Suite for Evaluating Pronouns in Machine Translation
Liane Guillou | Christian Hardmeier
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present PROTEST, a test suite for the evaluation of pronoun translation by MT systems. The test suite comprises 250 hand-selected pronoun tokens and an automatic evaluation method which compares the translations of pronouns in MT output with those in the reference translation. Pronoun translations that do not match the reference are referred for manual evaluation. PROTEST is designed to support analysis of system performance at the level of individual pronoun groups, rather than to provide a single aggregate measure over all pronouns. We wish to encourage detailed analyses to highlight issues in the handling of specific linguistic mechanisms by MT systems, thereby contributing to a better understanding of those problems involved in translating pronouns. We present two use cases for PROTEST: a) for measuring improvement/degradation of an incremental system change, and b) for comparing the performance of a group of systems whose design may be largely unrelated. Following the latter use case, we demonstrate the application of PROTEST to the evaluation of the systems submitted to the DiscoMT 2015 shared task on pronoun translation.

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A Neural Model for Part-of-Speech Tagging in Historical Texts
Christian Hardmeier
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Historical texts are challenging for natural language processing because they differ linguistically from modern texts and because of their lack of orthographical and grammatical standardisation. We use a character-level neural network to build a part-of-speech (POS) tagger that can process historical data directly without requiring a separate spelling normalisation stage. Its performance in a Swedish verb identification and a German POS tagging task is similar to that of a two-stage model. We analyse the performance of this tagger and a more traditional baseline system, discuss some of the remaining problems for tagging historical data and suggest how the flexibility of our neural tagger could be exploited to address diachronic divergences in morphology and syntax in early modern Swedish with the help of data from closely related languages.

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Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction
Liane Guillou | Christian Hardmeier | Preslav Nakov | Sara Stymne | Jörg Tiedemann | Yannick Versley | Mauro Cettolo | Bonnie Webber | Andrei Popescu-Belis
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Pronoun Prediction with Latent Anaphora Resolution
Christian Hardmeier
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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It-disambiguation and source-aware language models for cross-lingual pronoun prediction
Sharid Loáiciga | Liane Guillou | Christian Hardmeier
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Climbing Mont BLEU: The Strange World of Reachable High-BLEU Translations
Aaron Smith | Christian Hardmeier | Joerg Tiedemann
Proceedings of the 19th Annual Conference of the European Association for Machine Translation

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A Graphical Pronoun Analysis Tool for the PROTEST Pronoun Evaluation Test Suite
Christian Hardmeier | Liane Guillou
Proceedings of the 19th Annual Conference of the European Association for Machine Translation

2015

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Proceedings of the Second Workshop on Discourse in Machine Translation
Bonnie Webber | Marine Carpuat | Andrei Popescu-Belis | Christian Hardmeier
Proceedings of the Second Workshop on Discourse in Machine Translation

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Pronoun-Focused MT and Cross-Lingual Pronoun Prediction: Findings of the 2015 DiscoMT Shared Task on Pronoun Translation
Christian Hardmeier | Preslav Nakov | Sara Stymne | Jörg Tiedemann | Yannick Versley | Mauro Cettolo
Proceedings of the Second Workshop on Discourse in Machine Translation

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Part-of-Speech Driven Cross-Lingual Pronoun Prediction with Feed-Forward Neural Networks
Jimmy Callin | Christian Hardmeier | Jörg Tiedemann
Proceedings of the Second Workshop on Discourse in Machine Translation

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A Document-Level SMT System with Integrated Pronoun Prediction
Christian Hardmeier
Proceedings of the Second Workshop on Discourse in Machine Translation

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On Statistical Machine Translation and Translation Theory
Christian Hardmeier
Proceedings of the Second Workshop on Discourse in Machine Translation

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Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)
Deyi Xiong | Kevin Duh | Christian Hardmeier | Roberto Navigli
Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)

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Learning Structural Kernels for Natural Language Processing
Daniel Beck | Trevor Cohn | Christian Hardmeier | Lucia Specia
Transactions of the Association for Computational Linguistics, Volume 3

Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.

2014

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Anaphora Models and Reordering for Phrase-Based SMT
Christian Hardmeier | Sara Stymne | Jörg Tiedemann | Aaron Smith | Joakim Nivre
Proceedings of the Ninth Workshop on Statistical Machine Translation

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ParCor 1.0: A Parallel Pronoun-Coreference Corpus to Support Statistical MT
Liane Guillou | Christian Hardmeier | Aaron Smith | Jörg Tiedemann | Bonnie Webber
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We present ParCor, a parallel corpus of texts in which pronoun coreference ― reduced coreference in which pronouns are used as referring expressions ― has been annotated. The corpus is intended to be used both as a resource from which to learn systematic differences in pronoun use between languages and ultimately for developing and testing informed Statistical Machine Translation systems aimed at addressing the problem of pronoun coreference in translation. At present, the corpus consists of a collection of parallel English-German documents from two different text genres: TED Talks (transcribed planned speech), and EU Bookshop publications (written text). All documents in the corpus have been manually annotated with respect to the type and location of each pronoun and, where relevant, its antecedent. We provide details of the texts that we selected, the guidelines and tools used to support annotation and some corpus statistics. The texts in the corpus have already been translated into many languages, and we plan to expand the corpus into these other languages, as well as other genres, in the future.

2013

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Latent Anaphora Resolution for Cross-Lingual Pronoun Prediction
Christian Hardmeier | Jörg Tiedemann | Joakim Nivre
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Tunable Distortion Limits and Corpus Cleaning for SMT
Sara Stymne | Christian Hardmeier | Jörg Tiedemann | Joakim Nivre
Proceedings of the Eighth Workshop on Statistical Machine Translation

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Feature Weight Optimization for Discourse-Level SMT
Sara Stymne | Christian Hardmeier | Jörg Tiedemann | Joakim Nivre
Proceedings of the Workshop on Discourse in Machine Translation

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Statistical Machine Translation with Readability Constraints
Sara Stymne | Jörg Tiedemann | Christian Hardmeier | Joakim Nivre
Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013)

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Docent: A Document-Level Decoder for Phrase-Based Statistical Machine Translation
Christian Hardmeier | Sara Stymne | Jörg Tiedemann | Joakim Nivre
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations

2012

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Tree Kernels for Machine Translation Quality Estimation
Christian Hardmeier | Joakim Nivre | Jörg Tiedemann
Proceedings of the Seventh Workshop on Statistical Machine Translation

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Document-Wide Decoding for Phrase-Based Statistical Machine Translation
Christian Hardmeier | Joakim Nivre | Jörg Tiedemann
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

2011

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The Uppsala-FBK systems at WMT 2011
Christian Hardmeier | Jörg Tiedemann | Markus Saers | Marcello Federico | Prashant Mathur
Proceedings of the Sixth Workshop on Statistical Machine Translation

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Improving Machine Translation Quality Prediction with Syntactic Tree Kernels
Christian Hardmeier
Proceedings of the 15th Annual conference of the European Association for Machine Translation

2010

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FBK at WMT 2010: Word Lattices for Morphological Reduction and Chunk-Based Reordering
Christian Hardmeier | Arianna Bisazza | Marcello Federico
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

2009

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Using Linguistic Annotations in Statistical Machine Translation of Film Subtitles
Christian Hardmeier | Martin Volk
Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009)