Eric Xing

Also published as: Eric P. Xing


2020

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Data-to-Text Generation with Style Imitation
Shuai Lin | Wentao Wang | Zichao Yang | Xiaodan Liang | Frank F. Xu | Eric Xing | Zhiting Hu
Findings of the Association for Computational Linguistics: EMNLP 2020

Recent neural approaches to data-to-text generation have mostly focused on improving content fidelity while lacking explicit control over writing styles (e.g., sentence structures, word choices). More traditional systems use templates to determine the realization of text. Yet manual or automatic construction of high-quality templates is difficult, and a template acting as hard constraints could harm content fidelity when it does not match the record perfectly. We study a new way of stylistic control by using existing sentences as “soft” templates. That is, a model learns to imitate the writing style of any given exemplar sentence, with automatic adaptions to faithfully describe the record. The problem is challenging due to the lack of parallel data. We develop a neural approach that includes a hybrid attention-copy mechanism, learns with weak supervisions, and is enhanced with a new content coverage constraint. We conduct experiments in restaurants and sports domains. Results show our approach achieves stronger performance than a range of comparison methods. Our approach balances well between content fidelity and style control given exemplars that match the records to varying degrees.

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Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach
Bowen Tan | Lianhui Qin | Eric Xing | Zhiting Hu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Given a document and a target aspect (e.g., a topic of interest), aspect-based abstractive summarization attempts to generate a summary with respect to the aspect. Previous studies usually assume a small pre-defined set of aspects and fall short of summarizing on other diverse topics. In this work, we study summarizing on arbitrary aspects relevant to the document, which significantly expands the application of the task in practice. Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme, both of which integrate rich external knowledge sources such as ConceptNet and Wikipedia. Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents given pre-defined or arbitrary aspects.

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A Data-Centric Framework for Composable NLP Workflows
Zhengzhong Liu | Guanxiong Ding | Avinash Bukkittu | Mansi Gupta | Pengzhi Gao | Atif Ahmed | Shikun Zhang | Xin Gao | Swapnil Singhavi | Linwei Li | Wei Wei | Zecong Hu | Haoran Shi | Xiaodan Liang | Teruko Mitamura | Eric Xing | Zhiting Hu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization. We establish a unified open-source framework to support fast development of such sophisticated NLP workflows in a composable manner. The framework introduces a uniform data representation to encode heterogeneous results by a wide range of NLP tasks. It offers a large repository of processors for NLP tasks, visualization, and annotation, which can be easily assembled with full interoperability under the unified representation. The highly extensible framework allows plugging in custom processors from external off-the-shelf NLP and deep learning libraries. The whole framework is delivered through two modularized yet integratable open-source projects, namely Forte (for workflow infrastructure and NLP function processors) and Stave (for user interaction, visualization, and annotation).

2019

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Target-Guided Open-Domain Conversation
Jianheng Tang | Tiancheng Zhao | Chenyan Xiong | Xiaodan Liang | Eric Xing | Zhiting Hu
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Many real-world open-domain conversation applications have specific goals to achieve during open-ended chats, such as recommendation, psychotherapy, education, etc. We study the problem of imposing conversational goals on open-domain chat agents. In particular, we want a conversational system to chat naturally with human and proactively guide the conversation to a designated target subject. The problem is challenging as no public data is available for learning such a target-guided strategy. We propose a structured approach that introduces coarse-grained keywords to control the intended content of system responses. We then attain smooth conversation transition through turn-level supervised learning, and drive the conversation towards the target with discourse-level constraints. We further derive a keyword-augmented conversation dataset for the study. Quantitative and human evaluations show our system can produce meaningful and effective conversations, significantly improving over other approaches

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Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports
Baoyu Jing | Zeya Wang | Eric Xing
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Chest X-Ray (CXR) images are commonly used for clinical screening and diagnosis. Automatically writing reports for these images can considerably lighten the workload of radiologists for summarizing descriptive findings and conclusive impressions. The complex structures between and within sections of the reports pose a great challenge to the automatic report generation. Specifically, the section Impression is a diagnostic summarization over the section Findings; and the appearance of normality dominates each section over that of abnormality. Existing studies rarely explore and consider this fundamental structure information. In this work, we propose a novel framework which exploits the structure information between and within report sections for generating CXR imaging reports. First, we propose a two-stage strategy that explicitly models the relationship between Findings and Impression. Second, we design a novel co-operative multi-agent system that implicitly captures the imbalanced distribution between abnormality and normality. Experiments on two CXR report datasets show that our method achieves state-of-the-art performance in terms of various evaluation metrics. Our results expose that the proposed approach is able to generate high-quality medical reports through integrating the structure information.

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Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
Zhiting Hu | Haoran Shi | Bowen Tan | Wentao Wang | Zichao Yang | Tiancheng Zhao | Junxian He | Lianhui Qin | Di Wang | Xuezhe Ma | Zhengzhong Liu | Xiaodan Liang | Wanrong Zhu | Devendra Sachan | Eric Xing
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks that transform any inputs into natural language, such as machine translation, summarization, dialog, content manipulation, and so forth. With the design goals of modularity, versatility, and extensibility in mind, Texar extracts common patterns underlying the diverse tasks and methodologies, creates a library of highly reusable modules and functionalities, and allows arbitrary model architectures and algorithmic paradigms. In Texar, model architecture, inference, and learning processes are properly decomposed. Modules at a high concept level can be freely assembled or plugged in/swapped out. Texar is thus particularly suitable for researchers and practitioners to do fast prototyping and experimentation. The versatile toolkit also fosters technique sharing across different text generation tasks. Texar supports both TensorFlow and PyTorch, and is released under Apache License 2.0 at https://www.texar.io.

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Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
Mrinmaya Sachan | Avinava Dubey | Eduard H. Hovy | Tom M. Mitchell | Dan Roth | Eric P. Xing
Computational Linguistics, Volume 45, Issue 4 - December 2019

To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features that can be leveraged for various NLP tasks. In this article, we study some of these discourse features in multimedia text and what communicative function they fulfill in the context. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We conclude that the discourse and text layout features provide information that is complementary to lexical semantic information. Finally, we show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable.

2018

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Self-Training for Jointly Learning to Ask and Answer Questions
Mrinmaya Sachan | Eric Xing
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the NLP literature. At the same time, both require significant amounts of supervised data which is hard to obtain in many domains. To alleviate these issues, we propose a self-training method for jointly learning to ask as well as answer questions, leveraging unlabeled text along with labeled question answer pairs for learning. We evaluate our approach on four benchmark datasets: SQUAD, MS MARCO, WikiQA and TrecQA, and show significant improvements over a number of established baselines on both question answering and question generation tasks. We also achieved new state-of-the-art results on two competitive answer sentence selection tasks: WikiQA and TrecQA.

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Standardized Tests as benchmarks for Artificial Intelligence
Mrinmaya Sachan | Minjoon Seo | Hannaneh Hajishirzi | Eric Xing
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts

Standardized tests have recently been proposed as replacements to the Turing test as a driver for progress in AI (Clark, 2015). These include tests on understanding passages and stories and answering questions about them (Richardson et al., 2013; Rajpurkar et al., 2016a, inter alia), science question answering (Schoenick et al., 2016, inter alia), algebra word problems (Kushman et al., 2014, inter alia), geometry problems (Seo et al., 2015; Sachan et al., 2016), visual question answering (Antol et al., 2015), etc. Many of these tests require sophisticated understanding of the world, aiming to push the boundaries of AI. For this tutorial, we broadly categorize these tests into two categories: open domain tests such as reading comprehensions and elementary school tests where the goal is to find the support for an answer from the student curriculum, and closed domain tests such as intermediate level math and science tests (algebra, geometry, Newtonian physics problems, etc.). Unlike open domain tests, closed domain tests require the system to have significant domain knowledge and reasoning capabilities. For example, geometry questions typically involve a number of geometry primitives (lines, quadrilaterals, circles, etc) and require students to use axioms and theorems of geometry (Pythagoras theorem, alternating angles, etc) to solve them. These closed domains often have a formal logical basis and the question can be mapped to a formal language by semantic parsing. The formal question representation can then provided as an input to an expert system to solve the question.

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A Neural Architecture for Automated ICD Coding
Pengtao Xie | Eric Xing
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The International Classification of Diseases (ICD) provides a hierarchy of diagnostic codes for classifying diseases. Medical coding – which assigns a subset of ICD codes to a patient visit – is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and error prone. In this paper, we build a neural architecture for automated coding. It takes the diagnosis descriptions (DDs) of a patient as inputs and selects the most relevant ICD codes. This architecture contains four major ingredients: (1) tree-of-sequences LSTM encoding of code descriptions (CDs), (2) adversarial learning for reconciling the different writing styles of DDs and CDs, (3) isotonic constraints for incorporating the importance order among the assigned codes, and (4) attentional matching for performing many-to-one and one-to-many mappings from DDs to CDs. We demonstrate the effectiveness of the proposed methods on a clinical datasets with 59K patient visits.

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On the Automatic Generation of Medical Imaging Reports
Baoyu Jing | Pengtao Xie | Eric Xing
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Medical imaging is widely used in clinical practice for diagnosis and treatment. Report-writing can be error-prone for unexperienced physicians, and time-consuming and tedious for experienced physicians. To address these issues, we study the automatic generation of medical imaging reports. This task presents several challenges. First, a complete report contains multiple heterogeneous forms of information, including findings and tags. Second, abnormal regions in medical images are difficult to identify. Third, the reports are typically long, containing multiple sentences. To cope with these challenges, we (1) build a multi-task learning framework which jointly performs the prediction of tags and the generation of paragraphs, (2) propose a co-attention mechanism to localize regions containing abnormalities and generate narrations for them, (3) develop a hierarchical LSTM model to generate long paragraphs. We demonstrate the effectiveness of the proposed methods on two publicly available dataset.

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Texar: A Modularized, Versatile, and Extensible Toolbox for Text Generation
Zhiting Hu | Zichao Yang | Tiancheng Zhao | Haoran Shi | Junxian He | Di Wang | Xuezhe Ma | Zhengzhong Liu | Xiaodan Liang | Lianhui Qin | Devendra Singh Chaplot | Bowen Tan | Xingjiang Yu | Eric Xing
Proceedings of Workshop for NLP Open Source Software (NLP-OSS)

We introduce Texar, an open-source toolkit aiming to support the broad set of text generation tasks. Different from many existing toolkits that are specialized for specific applications (e.g., neural machine translation), Texar is designed to be highly flexible and versatile. This is achieved by abstracting the common patterns underlying the diverse tasks and methodologies, creating a library of highly reusable modules and functionalities, and enabling arbitrary model architectures and various algorithmic paradigms. The features make Texar particularly suitable for technique sharing and generalization across different text generation applications. The toolkit emphasizes heavily on extensibility and modularized system design, so that components can be freely plugged in or swapped out. We conduct extensive experiments and case studies to demonstrate the use and advantage of the toolkit.

2017

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Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
Lianhui Qin | Zhisong Zhang | Hai Zhao | Zhiting Hu | Eric Xing
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Implicit discourse relation classification is of great challenge due to the lack of connectives as strong linguistic cues, which motivates the use of annotated implicit connectives to improve the recognition. We propose a feature imitation framework in which an implicit relation network is driven to learn from another neural network with access to connectives, and thus encouraged to extract similarly salient features for accurate classification. We develop an adversarial model to enable an adaptive imitation scheme through competition between the implicit network and a rival feature discriminator. Our method effectively transfers discriminability of connectives to the implicit features, and achieves state-of-the-art performance on the PDTB benchmark.

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A Constituent-Centric Neural Architecture for Reading Comprehension
Pengtao Xie | Eric Xing
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Reading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task. In this paper, we study the RC problem on the Stanford Question Answering Dataset (SQuAD). Observing from the training set that most correct answers are centered around constituents in the parse tree, we design a constituent-centric neural architecture where the generation of candidate answers and their representation learning are both based on constituents and guided by the parse tree. Under this architecture, the search space of candidate answers can be greatly reduced without sacrificing the coverage of correct answers and the syntactic, hierarchical and compositional structure among constituents can be well captured, which contributes to better representation learning of the candidate answers. On SQuAD, our method achieves the state of the art performance and the ablation study corroborates the effectiveness of individual modules.

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Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks
Mrinmaya Sachan | Eric Xing
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)

Humans as well as animals are good at imitation. Inspired by this, the learning by demonstration view of machine learning learns to perform a task from detailed example demonstrations. In this paper, we introduce the task of question answering using natural language demonstrations where the question answering system is provided with detailed demonstrative solutions to questions in natural language. As a case study, we explore the task of learning to solve geometry problems using demonstrative solutions available in textbooks. We collect a new dataset of demonstrative geometry solutions from textbooks and explore approaches that learn to interpret these demonstrations as well as to use these interpretations to solve geometry problems. Our approaches show improvements over the best previously published system for solving geometry problems.

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From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems
Mrinmaya Sachan | Kumar Dubey | Eric Xing
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Textbooks are rich sources of information. Harvesting structured knowledge from textbooks is a key challenge in many educational applications. As a case study, we present an approach for harvesting structured axiomatic knowledge from math textbooks. Our approach uses rich contextual and typographical features extracted from raw textbooks. It leverages the redundancy and shared ordering across multiple textbooks to further refine the harvested axioms. These axioms are then parsed into rules that are used to improve the state-of-the-art in solving geometry problems.

2016

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Deep Neural Networks with Massive Learned Knowledge
Zhiting Hu | Zichao Yang | Ruslan Salakhutdinov | Eric Xing
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Easy Questions First? A Case Study on Curriculum Learning for Question Answering
Mrinmaya Sachan | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Learning Concept Taxonomies from Multi-modal Data
Hao Zhang | Zhiting Hu | Yuntian Deng | Mrinmaya Sachan | Zhicheng Yan | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Harnessing Deep Neural Networks with Logic Rules
Zhiting Hu | Xuezhe Ma | Zhengzhong Liu | Eduard Hovy | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Science Question Answering using Instructional Materials
Mrinmaya Sachan | Kumar Dubey | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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Machine Comprehension using Rich Semantic Representations
Mrinmaya Sachan | Eric Xing
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

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Incorporating Word Correlation Knowledge into Topic Modeling
Pengtao Xie | Diyi Yang | Eric Xing
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Learning Answer-Entailing Structures for Machine Comprehension
Mrinmaya Sachan | Kumar Dubey | Eric Xing | Matthew Richardson
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Entity Hierarchy Embedding
Zhiting Hu | Poyao Huang | Yuntian Deng | Yingkai Gao | Eric Xing
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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Spectral Unsupervised Parsing with Additive Tree Metrics
Ankur P. Parikh | Shay B. Cohen | Eric P. Xing
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Dynamic Language Models for Streaming Text
Dani Yogatama | Chong Wang | Bryan R. Routledge | Noah A. Smith | Eric P. Xing
Transactions of the Association for Computational Linguistics, Volume 2

We present a probabilistic language model that captures temporal dynamics and conditions on arbitrary non-linguistic context features. These context features serve as important indicators of language changes that are otherwise difficult to capture using text data by itself. We learn our model in an efficient online fashion that is scalable for large, streaming data. With five streaming datasets from two different genres—economics news articles and social media—we evaluate our model on the task of sequential language modeling. Our model consistently outperforms competing models.

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Language Modeling with Power Low Rank Ensembles
Ankur P. Parikh | Avneesh Saluja | Chris Dyer | Eric Xing
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

2012

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Topic Models, Latent Space Models, Sparse Coding, and All That: A Systematic Understanding of Probabilistic Semantic Extraction in Large Corpus
Eric Xing
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

2011

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Structured Databases of Named Entities from Bayesian Nonparametrics
Jacob Eisenstein | Tae Yano | William Cohen | Noah Smith | Eric Xing
Proceedings of the First workshop on Unsupervised Learning in NLP

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Discovering Sociolinguistic Associations with Structured Sparsity
Jacob Eisenstein | Noah A. Smith | Eric P. Xing
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Social Links from Latent Topics in Microblogs
Kriti Puniyani | Jacob Eisenstein | Shay B. Cohen | Eric Xing
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media

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Turbo Parsers: Dependency Parsing by Approximate Variational Inference
André Martins | Noah Smith | Eric Xing | Pedro Aguiar | Mário Figueiredo
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Staying Informed: Supervised and Semi-Supervised Multi-View Topical Analysis of Ideological Perspective
Amr Ahmed | Eric Xing
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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A Latent Variable Model for Geographic Lexical Variation
Jacob Eisenstein | Brendan O’Connor | Noah A. Smith | Eric P. Xing
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Concise Integer Linear Programming Formulations for Dependency Parsing
André Martins | Noah Smith | Eric Xing
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

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Stacking Dependency Parsers
André Filipe Torres Martins | Dipanjan Das | Noah A. Smith | Eric P. Xing
Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing

2006

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BiTAM: Bilingual Topic AdMixture Models for Word Alignment
Bing Zhao | Eric P. Xing
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

2005

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Bilingual Word Spectral Clustering for Statistical Machine Translation
Bing Zhao | Eric P. Xing | Alex Waibel
Proceedings of the ACL Workshop on Building and Using Parallel Texts