Salim Roukos

Also published as: S. Roukos


2021

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Bootstrapping Multilingual AMR with Contextual Word Alignments
Janaki Sheth | Young-Suk Lee | Ramón Fernandez Astudillo | Tahira Naseem | Radu Florian | Salim Roukos | Todd Ward
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

We develop high performance multilingual Abstract Meaning Representation (AMR) systems by projecting English AMR annotations to other languages with weak supervision. We achieve this goal by bootstrapping transformer-based multilingual word embeddings, in particular those from cross-lingual RoBERTa (XLM-R large). We develop a novel technique for foreign-text-to-English AMR alignment, using the contextual word alignment between English and foreign language tokens. This word alignment is weakly supervised and relies on the contextualized XLM-R word embeddings. We achieve a highly competitive performance that surpasses the best published results for German, Italian, Spanish and Chinese.

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A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering
Tahira Naseem | Srinivas Ravishankar | Nandana Mihindukulasooriya | Ibrahim Abdelaziz | Young-Suk Lee | Pavan Kapanipathi | Salim Roukos | Alfio Gliozzo | Alexander Gray
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic parse of the question is a rich source of relation information that is not taken advantage of. We propose a simple transformer-based neural model for relation linking that leverages the AMR semantic parse of a sentence. Our system significantly outperforms the state-of-the-art on 4 popular benchmark datasets. These are based on either DBpedia or Wikidata, demonstrating that our approach is effective across KGs.

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Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Pavan Kapanipathi | Ibrahim Abdelaziz | Srinivas Ravishankar | Salim Roukos | Alexander Gray | Ramón Fernandez Astudillo | Maria Chang | Cristina Cornelio | Saswati Dana | Achille Fokoue | Dinesh Garg | Alfio Gliozzo | Sairam Gurajada | Hima Karanam | Naweed Khan | Dinesh Khandelwal | Young-Suk Lee | Yunyao Li | Francois Luus | Ndivhuwo Makondo | Nandana Mihindukulasooriya | Tahira Naseem | Sumit Neelam | Lucian Popa | Revanth Gangi Reddy | Ryan Riegel | Gaetano Rossiello | Udit Sharma | G P Shrivatsa Bhargav | Mo Yu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

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A Multilingual Reading Comprehension System for more than 100 Languages
Anthony Ferritto | Sara Rosenthal | Mihaela Bornea | Kazi Hasan | Rishav Chakravarti | Salim Roukos | Radu Florian | Avi Sil
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

This paper presents M-GAAMA, a Multilingual Question Answering architecture and demo system. This is the first multilingual machine reading comprehension (MRC) demo which is able to answer questions in over 100 languages. M-GAAMA answers questions from a given passage in the same or different language. It incorporates several existing multilingual models that can be used interchangeably in the demo such as M-BERT and XLM-R. The M-GAAMA demo also improves language accessibility by incorporating the IBM Watson machine translation widget to provide additional capabilities to the user to see an answer in their desired language. We also show how M-GAAMA can be used in downstream tasks by incorporating it into an END-TO-END-QA system using CFO (Chakravarti et al., 2019). We experiment with our system architecture on the Multi-Lingual Question Answering (MLQA) and the COVID-19 CORD (Wang et al., 2020; Tang et al., 2020) datasets to provide insights into the performance of the system.

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Towards building a Robust Industry-scale Question Answering System
Rishav Chakravarti | Anthony Ferritto | Bhavani Iyer | Lin Pan | Radu Florian | Salim Roukos | Avi Sil
Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

Industry-scale NLP systems necessitate two features. 1. Robustness: “zero-shot transfer learning” (ZSTL) performance has to be commendable and 2. Efficiency: systems have to train efficiently and respond instantaneously. In this paper, we introduce the development of a production model called GAAMA (Go Ahead Ask Me Anything) which possess the above two characteristics. For robustness, it trains on the recently introduced Natural Questions (NQ) dataset. NQ poses additional challenges over older datasets like SQuAD: (a) QA systems need to read and comprehend an entire Wikipedia article rather than a small passage, and (b) NQ does not suffer from observation bias during construction, resulting in less lexical overlap between the question and the article. GAAMA consists of Attention-over-Attention, diversity among attention heads, hierarchical transfer learning, and synthetic data augmentation while being computationally inexpensive. Building on top of the powerful BERTQA model, GAAMA provides a ∼2.0% absolute boost in F1 over the industry-scale state-of-the-art (SOTA) system on NQ. Further, we show that GAAMA transfers zero-shot to unseen real life and important domains as it yields respectable performance on two benchmarks: the BioASQ and the newly introduced CovidQA datasets.

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Pushing the Limits of AMR Parsing with Self-Learning
Young-Suk Lee | Ramón Fernandez Astudillo | Tahira Naseem | Revanth Gangi Reddy | Radu Florian | Salim Roukos
Findings of the Association for Computational Linguistics: EMNLP 2020

Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR. At the same time, self-learning techniques have helped push the performance boundaries of other natural language processing applications, such as machine translation or question answering. In this paper, we explore different ways in which trained models can be applied to improve AMR parsing performance, including generation of synthetic text and AMR annotations as well as refinement of actions oracle. We show that, without any additional human annotations, these techniques improve an already performant parser and achieve state-of-the-art results on AMR 1.0 and AMR 2.0.

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The TechQA Dataset
Vittorio Castelli | Rishav Chakravarti | Saswati Dana | Anthony Ferritto | Radu Florian | Martin Franz | Dinesh Garg | Dinesh Khandelwal | Scott McCarley | Michael McCawley | Mohamed Nasr | Lin Pan | Cezar Pendus | John Pitrelli | Saurabh Pujar | Salim Roukos | Andrzej Sakrajda | Avi Sil | Rosario Uceda-Sosa | Todd Ward | Rong Zhang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We introduce TECHQA, a domain-adaptation question answering dataset for the technical support domain. The TECHQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size – 600 training, 310 dev, and 490 evaluation question/answer pairs – thus reflecting the cost of creating large labeled datasets with actual data. Hence, TECHQA is meant to stimulate research in domain adaptation rather than as a resource to build QA systems from scratch. TECHQA was obtained by crawling the IBMDeveloper and DeveloperWorks forums for questions with accepted answers provided in an IBM Technote—a technical document that addresses a specific technical issue. We also release a collection of the 801,998 Technotes available on the web as of April 4, 2019 as a companion resource that can be used to learn representations of the IT domain language.

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GPT-too: A Language-Model-First Approach for AMR-to-Text Generation
Manuel Mager | Ramón Fernandez Astudillo | Tahira Naseem | Md Arafat Sultan | Young-Suk Lee | Radu Florian | Salim Roukos
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Abstract Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10 dataset, including the recent use of transformer architectures. In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach.

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Multi-Stage Pre-training for Low-Resource Domain Adaptation
Rong Zhang | Revanth Gangi Reddy | Md Arafat Sultan | Vittorio Castelli | Anthony Ferritto | Radu Florian | Efsun Sarioglu Kayi | Salim Roukos | Avi Sil | Todd Ward
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Transfer learning techniques are particularly useful for NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain. Current approaches directly adapt a pretrained language model (LM) on in-domain text before fine-tuning to downstream tasks. We show that extending the vocabulary of the LM with domain-specific terms leads to further gains. To a bigger effect, we utilize structure in the unlabeled data to create auxiliary synthetic tasks, which helps the LM transfer to downstream tasks. We apply these approaches incrementally on a pretrained Roberta-large LM and show considerable performance gain on three tasks in the IT domain: Extractive Reading Comprehension, Document Ranking and Duplicate Question Detection.

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ARES: A Reading Comprehension Ensembling Service
Anthony Ferritto | Lin Pan | Rishav Chakravarti | Salim Roukos | Radu Florian | J. William Murdock | Avi Sil
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

We introduce ARES (A Reading Comprehension Ensembling Service): a novel Machine Reading Comprehension (MRC) demonstration system which utilizes an ensemble of models to increase F1 by 2.3 points. While many of the top leaderboard submissions in popular MRC benchmarks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questions (NQ) use model ensembles, the accompanying papers do not publish their ensembling strategies. In this work, we detail and evaluate various ensembling strategies using the NQ dataset. ARES leverages the CFO (Chakravarti et al., 2019) and ReactJS distributed frameworks to provide a scalable interactive Question Answering experience that capitalizes on the agreement (or lack thereof) between models to improve the answer visualization experience.

2019

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CFO: A Framework for Building Production NLP Systems
Rishav Chakravarti | Cezar Pendus | Andrzej Sakrajda | Anthony Ferritto | Lin Pan | Michael Glass | Vittorio Castelli | J. William Murdock | Radu Florian | Salim Roukos | Avi Sil
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

This paper introduces a novel orchestration framework, called CFO (Computation Flow Orchestrator), for building, experimenting with, and deploying interactive NLP (Natural Language Processing) and IR (Information Retrieval) systems to production environments. We then demonstrate a question answering system built using this framework which incorporates state-of-the-art BERT based MRC (Machine Reading Com- prehension) with IR components to enable end-to-end answer retrieval. Results from the demo system are shown to be high quality in both academic and industry domain specific settings. Finally, we discuss best practices when (pre-)training BERT based MRC models for production systems. Screencast links: - Short video (< 3 min): http: //ibm.biz/gaama_demo - Supplementary long video (< 13 min): http://ibm.biz/gaama_cfo_demo

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Rewarding Smatch: Transition-Based AMR Parsing with Reinforcement Learning
Tahira Naseem | Abhishek Shah | Hui Wan | Radu Florian | Salim Roukos | Miguel Ballesteros
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Our work involves enriching the Stack-LSTM transition-based AMR parser (Ballesteros and Al-Onaizan, 2017) by augmenting training with Policy Learning and rewarding the Smatch score of sampled graphs. In addition, we also combined several AMR-to-text alignments with an attention mechanism and we supplemented the parser with pre-processed concept identification, named entities and contextualized embeddings. We achieve a highly competitive performance that is comparable to the best published results. We show an in-depth study ablating each of the new components of the parser.

2014

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A novel use of MT in the development of a text level analytic for language learning
Carol Van Ess-Dykema | Salim Roukos | Amy Weinberg
Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Users Track

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Invited Talk: IBM Cognitive Computing - An NLP Renaissance!
Salim Roukos
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Adaptive HTER Estimation for Document-Specific MT Post-Editing
Fei Huang | Jian-Ming Xu | Abraham Ittycheriah | Salim Roukos
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2011

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A Correction Model for Word Alignments
J. Scott McCarley | Abraham Ittycheriah | Salim Roukos | Bing Xiang | Jian-ming Xu
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Improving Mention Detection Robustness to Noisy Input
Radu Florian | John Pitrelli | Salim Roukos | Imed Zitouni
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Learning to Predict Readability using Diverse Linguistic Features
Rohit Kate | Xiaoqiang Luo | Siddharth Patwardhan | Martin Franz | Radu Florian | Raymond Mooney | Salim Roukos | Chris Welty
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2009

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Real Time Translation Services at IBM
David Lubensky | Salim Roukos
Proceedings of Machine Translation Summit XII: Commercial MT User Program

2007

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Direct Translation Model 2
Abraham Ittycheriah | Salim Roukos
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

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Extracting Social Networks and Biographical Facts From Conversational Speech Transcripts
Hongyan Jing | Nanda Kambhatla | Salim Roukos
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

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Rosetta: an analyst’s co-pilot
Salim Roukos
Proceedings of the Third International Workshop on Spoken Language Translation: Plenaries

2005

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A Maximum Entropy Word Aligner for Arabic-English Machine Translation
Abraham Ittycheriah | Salim Roukos
Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

2004

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A Statistical Model for Multilingual Entity Detection and Tracking
R. Florian | H. Hassan | A. Ittycheriah | H. Jing | N. Kambhatla | X. Luo | N. Nicolov | S. Roukos
Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics: HLT-NAACL 2004

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A Mention-Synchronous Coreference Resolution Algorithm Based On the Bell Tree
Xiaoqiang Luo | Abe Ittycheriah | Hongyan Jing | Nanda Kambhatla | Salim Roukos
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)

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IBM spoken language translation system evaluation
Young-Suk Lee | Salim Roukos
Proceedings of the First International Workshop on Spoken Language Translation: Evaluation Campaign

2003

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Identifying and Tracking Entity Mentions in a Maximum Entropy Framework
Abraham Ittycheriah | Lucian Lita | Nanda Kambhatla | Nicolas Nicolov | Salim Roukos | Margo Stys
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

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Automatic Derivation of Surface Text Patterns for a Maximum Entropy Based Question Answering System
Deepak Ravichandran | Abraham Ittycheriah | Salim Roukos
Companion Volume of the Proceedings of HLT-NAACL 2003 - Short Papers

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TIPS: A Translingual Information Processing System
Yaser Al-Onaizan | Radu Florian | Martin Franz | Hany Hassan | Young-Suk Lee | J. Scott McCarley | Kishore Papineni | Salim Roukos | Jeffrey Sorensen | Christoph Tillmann | Todd Ward | Fei Xia
Companion Volume of the Proceedings of HLT-NAACL 2003 - Demonstrations

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tRuEcasIng
Lucian Vlad Lita | Abe Ittycheriah | Salim Roukos | Nanda Kambhatla
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

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Language Model Based Arabic Word Segmentation
Young-Suk Lee | Kishore Papineni | Salim Roukos | Ossama Emam | Hany Hassan
Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics

2002

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A Flexible Framework for Developing Mixed-Initiative Dialog Systems
Judith Hochberg | Nanda Kambhatla | Salim Roukos
Proceedings of the Third SIGdial Workshop on Discourse and Dialogue

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Active Learning for Statistical Natural Language Parsing
Min Tang | Xiaoqiang Luo | Salim Roukos
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Bleu: a Method for Automatic Evaluation of Machine Translation
Kishore Papineni | Salim Roukos | Todd Ward | Wei-Jing Zhu
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

1998

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Fast document translation for cross-language information retrieval
J.Scott McCarley | Salim Roukos
Proceedings of the Third Conference of the Association for Machine Translation in the Americas: Technical Papers

We describe a statistical algorithm for machine translation intended to provide translations of large document collections at speeds far in excess of traditional machine translation systems, and of sufficiently high quality to perform information retrieval on the translated document collections. The model is trained from a parallel corpus and is capable of disambiguating senses of words. Information retrieval (IR) experiments on a French language dataset from a recent cross-language information retrieval evaluation yields results superior to those obtained by participants in the evaluation, and confirm the importance of word sense disambiugation in cross-language information retrieval.

1997

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Fertility Models for Statistical Natural Language Understanding
Stephen Della Pietra | Mark Epstein | Salim Roukos | Todd Ward
35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics

1996

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An Iterative Algorithm to Build Chinese Language Models
Xiaoqiang Luo | Salim Roukos
34th Annual Meeting of the Association for Computational Linguistics

1994

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A Maximum Entropy Model for Prepositional Phrase Attachment
Adwait Ratnaparkhi | Jeff Reynar | Salim Roukos
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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Decision Tree Parsing using a Hidden Derivation Model
F. Jelinek | J. Lafferty | D. Magerman | R. Mercer | A. Ratnaparkhi | S. Roukos
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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Automatic Extraction of Grammars From Annotated Text
Salim Roukos
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

1993

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Adaptive Language Modeling Using the Maximum Entropy Principle
Raymond Lau | Ronald Rosenfeld | Salim Roukos
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Automatic Extraction of Grammars From Annotated Text
Salim Roukos
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Towards History-based Grammars: Using Richer Models for Probabilistic Parsing
Ezra Black | Fred Jelinek | John Lafrerty | David M. Magerman | Robert Mercer | Salim Roukos
31st Annual Meeting of the Association for Computational Linguistics

1992

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Development and Evaluation of a Broad-Coverage Probabilistic Grammar of English-Language Computer Manuals
Ezra Black | John Lafferty | Salim Roukos
30th Annual Meeting of the Association for Computational Linguistics

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Adaptive Language Modeling Using Minimum Discriminant Estimation
S. Della Pietra | V. Della Pietra | R. L. Mercer | S. Roukos
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Decision Tree Models Applied to the Labeling of Text with Parts-of-Speech
Ezra Black | Fred Jelinek | John Lafferty | Robert Mercer | Salim Roukos
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

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Towards History-based Grammars: Using Richer Models for Probabilistic Parsing
Ezra Black | Fred Jelinek | John Lafferty | David M. Magerman | Robert Mercer | Salim Roukos
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

1991

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Session 7: Natural Language II
Salim Roukos
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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A Dynamic Language Model for Speech Recognition
F. Jelinek | B. Merialdo | S. Roukos | M. Strauss
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

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A Procedure for Quantitatively Comparing the Syntactic Coverage of English Grammars
E. Black | S. Abney | D. Flickenger | C. Gdaniec | R. Grishman | P. Harrison | D. Hindle | R. Ingria | F. Jelinek | J. Klavans | M. Liberman | M. Marcus | S. Roukos | B. Santorini | T. Strzalkowski
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

1989

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Integrating Speech and Natural Language
Salim Roukos
Speech and Natural Language: Proceedings of a Workshop Held at Philadelphia, Pennsylvania, February 21-23, 1989

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The BBN Spoken Language System
Sean Boisen | Yen-Lu Chow | Andrew Haas | Robert Ingria | Salim Roukos | David Stallard
Speech and Natural Language: Proceedings of a Workshop Held at Philadelphia, Pennsylvania, February 21-23, 1989

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