Stuart M. Shieber

Also published as: Stuart Shieber


2021

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Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models
Matthew Finlayson | Aaron Mueller | Sebastian Gehrmann | Stuart Shieber | Tal Linzen | Yonatan Belinkov
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Targeted syntactic evaluations have demonstrated the ability of language models to perform subject-verb agreement given difficult contexts. To elucidate the mechanisms by which the models accomplish this behavior, this study applies causal mediation analysis to pre-trained neural language models. We investigate the magnitude of models’ preferences for grammatical inflections, as well as whether neurons process subject-verb agreement similarly across sentences with different syntactic structures. We uncover similarities and differences across architectures and model sizes—notably, that larger models do not necessarily learn stronger preferences. We also observe two distinct mechanisms for producing subject-verb agreement depending on the syntactic structure of the input sentence. Finally, we find that language models rely on similar sets of neurons when given sentences with similar syntactic structure.

2020

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Linguistic Features for Readability Assessment
Tovly Deutsch | Masoud Jasbi | Stuart Shieber
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

Readability assessment aims to automatically classify text by the level appropriate for learning readers. Traditional approaches to this task utilize a variety of linguistically motivated features paired with simple machine learning models. More recent methods have improved performance by discarding these features and utilizing deep learning models. However, it is unknown whether augmenting deep learning models with linguistically motivated features would improve performance further. This paper combines these two approaches with the goal of improving overall model performance and addressing this question. Evaluating on two large readability corpora, we find that, given sufficient training data, augmenting deep learning models with linguistically motivated features does not improve state-of-the-art performance. Our results provide preliminary evidence for the hypothesis that the state-of-the-art deep learning models represent linguistic features of the text related to readability. Future research on the nature of representations formed in these models can shed light on the learned features and their relations to linguistically motivated ones hypothesized in traditional approaches.

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Probing Neural Dialog Models for Conversational Understanding
Abdelrhman Saleh | Tovly Deutsch | Stephen Casper | Yonatan Belinkov | Stuart Shieber
Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI

The predominant approach to open-domain dialog generation relies on end-to-end training of neural models on chat datasets. However, this approach provides little insight as to what these models learn (or do not learn) about engaging in dialog. In this study, we analyze the internal representations learned by neural open-domain dialog systems and evaluate the quality of these representations for learning basic conversational skills. Our results suggest that standard open-domain dialog systems struggle with answering questions, inferring contradiction, and determining the topic of conversation, among other tasks. We also find that the dyadic, turn-taking nature of dialog is not fully leveraged by these models. By exploring these limitations, we highlight the need for additional research into architectures and training methods that can better capture high-level information about dialog.

2019

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On Evaluating the Generalization of LSTM Models in Formal Languages
Mirac Suzgun | Yonatan Belinkov | Stuart M. Shieber
Proceedings of the Society for Computation in Linguistics (SCiL) 2019

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LSTM Networks Can Perform Dynamic Counting
Mirac Suzgun | Yonatan Belinkov | Stuart Shieber | Sebastian Gehrmann
Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges

In this paper, we systematically assess the ability of standard recurrent networks to perform dynamic counting and to encode hierarchical representations. All the neural models in our experiments are designed to be small-sized networks both to prevent them from memorizing the training sets and to visualize and interpret their behaviour at test time. Our results demonstrate that the Long Short-Term Memory (LSTM) networks can learn to recognize the well-balanced parenthesis language (Dyck-1) and the shuffles of multiple Dyck-1 languages, each defined over different parenthesis-pairs, by emulating simple real-time k-counter machines. To the best of our knowledge, this work is the first study to introduce the shuffle languages to analyze the computational power of neural networks. We also show that a single-layer LSTM with only one hidden unit is practically sufficient for recognizing the Dyck-1 language. However, none of our recurrent networks was able to yield a good performance on the Dyck-2 language learning task, which requires a model to have a stack-like mechanism for recognition.

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On Adversarial Removal of Hypothesis-only Bias in Natural Language Inference
Yonatan Belinkov | Adam Poliak | Stuart Shieber | Benjamin Van Durme | Alexander Rush
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Popular Natural Language Inference (NLI) datasets have been shown to be tainted by hypothesis-only biases. Adversarial learning may help models ignore sensitive biases and spurious correlations in data. We evaluate whether adversarial learning can be used in NLI to encourage models to learn representations free of hypothesis-only biases. Our analyses indicate that the representations learned via adversarial learning may be less biased, with only small drops in NLI accuracy.

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Don’t Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
Yonatan Belinkov | Adam Poliak | Stuart Shieber | Benjamin Van Durme | Alexander Rush
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Natural Language Inference (NLI) datasets often contain hypothesis-only biases—artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise. We evaluate our methods on synthetic and existing NLI datasets by training on datasets containing biases and testing on datasets containing no (or different) hypothesis-only biases. Our results indicate that these methods can make NLI models more robust to dataset-specific artifacts, transferring better than a baseline architecture in 9 out of 12 NLI datasets. Additionally, we provide an extensive analysis of the interplay of our methods with known biases in NLI datasets, as well as the effects of encouraging models to ignore biases and fine-tuning on target datasets.

2018

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Learning Neural Templates for Text Generation
Sam Wiseman | Stuart Shieber | Alexander Rush
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

While neural, encoder-decoder models have had significant empirical success in text generation, there remain several unaddressed problems with this style of generation. Encoder-decoder models are largely (a) uninterpretable, and (b) difficult to control in terms of their phrasing or content. This work proposes a neural generation system using a hidden semi-markov model (HSMM) decoder, which learns latent, discrete templates jointly with learning to generate. We show that this model learns useful templates, and that these templates make generation both more interpretable and controllable. Furthermore, we show that this approach scales to real data sets and achieves strong performance nearing that of encoder-decoder text generation models.

2017

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Reflexives and Reciprocals in Synchronous Tree Adjoining Grammar
Cristina Aggazzotti | Stuart M. Shieber
Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms

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Challenges in Data-to-Document Generation
Sam Wiseman | Stuart Shieber | Alexander Rush
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Recent neural models have shown significant progress on the problem of generating short descriptive texts conditioned on a small number of database records. In this work, we suggest a slightly more difficult data-to-text generation task, and investigate how effective current approaches are on this task. In particular, we introduce a new, large-scale corpus of data records paired with descriptive documents, propose a series of extractive evaluation methods for analyzing performance, and obtain baseline results using current neural generation methods. Experiments show that these models produce fluent text, but fail to convincingly approximate human-generated documents. Moreover, even templated baselines exceed the performance of these neural models on some metrics, though copy- and reconstruction-based extensions lead to noticeable improvements.

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Adapting Sequence Models for Sentence Correction
Allen Schmaltz | Yoon Kim | Alexander Rush | Stuart Shieber
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.

2016

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Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction
Allen Schmaltz | Yoon Kim | Alexander M. Rush | Stuart Shieber
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

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Antecedent Prediction Without a Pipeline
Sam Wiseman | Alexander M. Rush | Stuart Shieber
Proceedings of the Workshop on Coreference Resolution Beyond OntoNotes (CORBON 2016)

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Learning Global Features for Coreference Resolution
Sam Wiseman | Alexander M. Rush | Stuart M. Shieber
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Word Ordering Without Syntax
Allen Schmaltz | Alexander M. Rush | Stuart Shieber
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

2015

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Learning Anaphoricity and Antecedent Ranking Features for Coreference Resolution
Sam Wiseman | Alexander M. Rush | Stuart Shieber | Jason Weston
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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Eliciting and Annotating Uncertainty in Spoken Language
Heather Pon-Barry | Stuart Shieber | Nicholas Longenbaugh
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

A major challenge in the field of automatic recognition of emotion and affect in speech is the subjective nature of affect labels. The most common approach to acquiring affect labels is to ask a panel of listeners to rate a corpus of spoken utterances along one or more dimensions of interest. For applications ranging from educational technology to voice search to dictation, a speaker’s level of certainty is a primary dimension of interest. In such applications, we would like to know the speaker’s actual level of certainty, but past research has only revealed listeners’ perception of the speaker’s level of certainty. In this paper, we present a method for eliciting spoken utterances using stimuli that we design such that they have a quantitative, crowdsourced legibility score. While we cannot control a speaker’s actual internal level of certainty, the use of these stimuli provides a better estimate of internal certainty compared to existing speech corpora. The Harvard Uncertainty Speech Corpus, containing speech data, certainty annotations, and prosodic features, is made available to the research community.

2013

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A Context Free TAG Variant
Ben Swanson | Elif Yamangil | Eugene Charniak | Stuart Shieber
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Nonparametric Bayesian Inference and Efficient Parsing for Tree-adjoining Grammars
Elif Yamangil | Stuart M. Shieber
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Estimating Compact Yet Rich Tree Insertion Grammars
Elif Yamangil | Stuart Shieber
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2010

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Complexity, Parsing, and Factorization of Tree-Local Multi-Component Tree-Adjoining Grammar
Rebecca Nesson | Giorgio Satta | Stuart M. Shieber
Computational Linguistics, Volume 36, Issue 3 - September 2010

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Bayesian Synchronous Tree-Substitution Grammar Induction and Its Application to Sentence Compression
Elif Yamangil | Stuart M. Shieber
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

2009

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Efficiently Parsable Extensions to Tree-Local Multicomponent TAG
Rebecca Nesson | Stuart Shieber
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics

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The Importance of Sub-Utterance Prosody in Predicting Level of Certainty
Heather Pon-Barry | Stuart Shieber
Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers

2008

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Synchronous Vector TAG for Syntax and Semantics: Control Verbs, Relative Clauses, and Inverse Linking
Rebecca Nesson | Stuart Shieber
Proceedings of the Ninth International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+9)

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Optimal k-arization of Synchronous Tree-Adjoining Grammar
Rebecca Nesson | Giorgio Satta | Stuart M. Shieber
Proceedings of ACL-08: HLT

2007

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Extraction Phenomena in Synchronous TAG Syntax and Semantics
Rebecca Nesson | Stuart M. Shieber
Proceedings of SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation

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Probabilistic Synchronous Tree-Adjoining Grammars for Machine Translation: The Argument from Bilingual Dictionaries
Stuart M. Shieber
Proceedings of SSST, NAACL-HLT 2007 / AMTA Workshop on Syntax and Structure in Statistical Translation

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Synchronous Grammars and Transducers: Good News and Bad News
Stuart Shieber
Proceedings of the Tenth International Conference on Parsing Technologies

2006

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Induction of Probabilistic Synchronous Tree-Insertion Grammars for Machine Translation
Rebecca Nesson | Stuart Shieber | Alexander Rush
Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers

The more expressive and flexible a base formalism for machine translation is, the less efficient parsing of it will be. However, even among formalisms with the same parse complexity, some formalisms better realize the desired characteristics for machine translation formalisms than others. We introduce a particular formalism, probabilistic synchronous tree-insertion grammar (PSTIG) that we argue satisfies the desiderata optimally within the class of formalisms that can be parsed no less efficiently than context-free grammars and demonstrate that it outperforms state-of-the-art word-based and phrase-based finite-state translation models on training and test data taken from the EuroParl corpus (Koehn, 2005). We then argue that a higher level of translation quality can be achieved by hybridizing our in- duced model with elementary structures produced using supervised techniques such as those of Groves et al. (2004).

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Towards Robust Context-Sensitive Sentence Alignment for Monolingual Corpora
Rani Nelken | Stuart M. Shieber
11th Conference of the European Chapter of the Association for Computational Linguistics

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Unifying Synchronous Tree Adjoining Grammars and Tree Transducers via Bimorphisms
Stuart M. Shieber
11th Conference of the European Chapter of the Association for Computational Linguistics

2005

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Arabic Diacritization Using Weighted Finite-State Transducers
Rani Nelken | Stuart M. Shieber
Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages

2004

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A learning approach to improving sentence-level MT evaluation
Alex Kulesza | Stuart M. Shieber
Proceedings of the 10th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

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Unifying Annotated Discourse Hierarchies to Create a Gold Standard
Marco Carbone | Ya’akov Gal | Stuart Shieber | Barbara Grosz
Proceedings of the 5th SIGdial Workshop on Discourse and Dialogue at HLT-NAACL 2004

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Synchronous Grammars as Tree Transducers
Stuart M. Shieber
Proceedings of the 7th International Workshop on Tree Adjoining Grammar and Related Formalisms

2003

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Comma Restoration Using Constituency Information
Stuart M. Shieber | Xiaopeng Tao
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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Partially Ordered Multiset Context-free Grammars and Free-word-order Parsing
Mark-Jan Nederhof | Giorgio Satta | Stuart Shieber
Proceedings of the Eighth International Conference on Parsing Technologies

We present a new formalism, partially ordered multiset context-free grammars (poms-CFG), along with an Earley-style parsing algorithm. The formalism, which can be thought of as a generalization of context-free grammars with partially ordered right-hand sides, is of interest in its own right, and also as infrastructure for obtaining tighter complexity bounds for more expressive context-free formalisms intended to express free or multiple word-order, such as ID/LP grammars. We reduce ID/LP grammars to poms-grammars, thereby getting finer-grained bounds on the parsing complexity of ID/LP grammars. We argue that in practice, the width of attested ID/LP grammars is small, yielding effectively polynomial time complexity for ID/LP grammar parsing.

2002

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The LinGO Redwoods Treebank: Motivation and Preliminary Applications
Stephan Oepen | Kristina Toutanova | Stuart Shieber | Christopher Manning | Dan Flickinger | Thorsten Brants
COLING 2002: The 17th International Conference on Computational Linguistics: Project Notes

1997

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Anaphoric Dependencies in Ellipsis
Andrew Kehler | Stuart Shieber
Computational Linguistics, Volume 23, Number 3, September 1997

1994

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An Alternative Conception of Tree-Adjoining Derivation
Yves Schabes | Stuart M. Shieber
Computational Linguistics, Volume 20, Number 1, March 1994

1993

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The problem of logical form equivalence
Stuart M. Shieber
Computational Linguistics, Volume 19, Number 1, March 1993, Special Issue on Using Large Corpora: I

1992

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An Alternative Conception of Tree-Adjoining Derivation
Yves Schabes | Stuart M. Shieber
30th Annual Meeting of the Association for Computational Linguistics

1990

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Synchronous Tree-Adjoining Grammars
Stuart M. Shieber | Yves Schabes
COLING 1990 Volume 3: Papers presented to the 13th International Conference on Computational Linguistics

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Generation and Synchronous Tree-Adjoining Grammars
Stuart M. Shieber | Yves Schabes
Proceedings of the Fifth International Workshop on Natural Language Generation

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Formal properties of Synchronous Tree-Adjoining Grammars
Stuart Shieber
Proceedings of the First International Workshop on Tree Adjoining Grammar and Related Frameworks (TAG+1)

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Semantic-Head-Driven Generation
Stuart M. Shieber | Gertjan van Noord | Fernando C. N. Pereira | Robert C. Moore
Computational Linguistics, Volume 16, Number 1, March 1990

1989

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A Semantic-Head-Driven Generation Algorithm for Unification-Based Formalisms
Stuart M. Shieber | Gertjan van Noord | Robert C. Moore | Fernando C. N. Pereira
27th Annual Meeting of the Association for Computational Linguistics

1988

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A Uniform Architecture for Parsing and Generation
Stuart M. Shieber
Coling Budapest 1988 Volume 2: International Conference on Computational Linguistics

1987

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An Algorithm for Generating Quantifier Scopings
Jerry R. Hobbs | Stuart M. Shieber
Computational Linguistics, Formerly the American Journal of Computational Linguistics, Volume 13, Numbers 1-2, January-June 1987

1986

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A Simple Reconstruction of GPSG
Stuart M. Shieber
Coling 1986 Volume 1: The 11th International Conference on Computational Linguistics

1985

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Using Restriction to Extend Parsing Algorithms for Complex-Feature-Based Formalisms
Stuart M. Shieber
23rd Annual Meeting of the Association for Computational Linguistics

1984

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The Semantics of Grammar Formalisms Seen as Computer Languages
Fernando C. N. Pereira | Stuart M. Shieber
10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics

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The Design of a Computer Language for Linguistic Information
Stuart M. Shieber
10th International Conference on Computational Linguistics and 22nd Annual Meeting of the Association for Computational Linguistics

1983

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Formal Constraints on Metarules
Stuart M. Shieber | Swan U. Stucky | Hans Uszkoreit | Jane J. Robinson
21st Annual Meeting of the Association for Computational Linguistics

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Sentence Disambiguation by a Shift-Reduce Parsing Technique
Stuart M. Shieber
21st Annual Meeting of the Association for Computational Linguistics

1982

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Translating English Into Logical Form
Stanley J. Rosenschein | Stuart M. Shieber
20th Annual Meeting of the Association for Computational Linguistics