Mark Steedman

Also published as: M. Steedman


2021

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Modeling Incremental Language Comprehension in the Brain with Combinatory Categorial Grammar
Miloš Stanojević | Shohini Bhattasali | Donald Dunagan | Luca Campanelli | Mark Steedman | Jonathan Brennan | John Hale
Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics

Hierarchical sentence structure plays a role in word-by-word human sentence comprehension, but it remains unclear how best to characterize this structure and unknown how exactly it would be recognized in a step-by-step process model. With a view towards sharpening this picture, we model the time course of hemodynamic activity within the brain during an extended episode of naturalistic language comprehension using Combinatory Categorial Grammar (CCG). CCG has well-defined incremental parsing algorithms, surface compositional semantics, and can explain long-range dependencies as well as complicated cases of coordination. We find that CCG-derived predictors improve a regression model of fMRI time course in six language-relevant brain regions, over and above predictors derived from context-free phrase structure. Adding a special Revealing operator to CCG parsing, one designed to handle right-adjunction, improves the fit in three of these regions. This evidence for CCG from neuroimaging bolsters the more general case for mildly context-sensitive grammars in the cognitive science of language.

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Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer
Tianyi Li | Sujian Li | Mark Steedman
Proceedings of the 17th International Conference on Parsing Technologies and the IWPT 2021 Shared Task on Parsing into Enhanced Universal Dependencies (IWPT 2021)

Strong and affordable in-domain data is a desirable asset when transferring trained semantic parsers to novel domains. As previous methods for semi-automatically constructing such data cannot handle the complexity of realistic SQL queries, we propose to construct SQL queries via context-dependent sampling, and introduce the concept of topic. Along with our SQL query construction method, we propose a novel pipeline of semi-automatic Text-to-SQL dataset construction that covers the broad space of SQL queries. We show that the created dataset is comparable with expert annotation along multiple dimensions, and is capable of improving domain transfer performance for SOTA semantic parsers.

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Fine-grained General Entity Typing in German using GermaNet
Sabine Weber | Mark Steedman
Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)

Fine-grained entity typing is important to tasks like relation extraction and knowledge base construction. We find however, that fine-grained entity typing systems perform poorly on general entities (e.g. “ex-president”) as compared to named entities (e.g. “Barack Obama”). This is due to a lack of general entities in existing training data sets. We show that this problem can be mitigated by automatically generating training data from WordNets. We use a German WordNet equivalent, GermaNet, to automatically generate training data for German general entity typing. We use this data to supplement named entity data to train a neural fine-grained entity typing system. This leads to a 10% improvement in accuracy of the prediction of level 1 FIGER types for German general entities, while decreasing named entity type prediction accuracy by only 1%.

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Modality and Negation in Event Extraction
Sander Bijl de Vroe | Liane Guillou | Miloš Stanojević | Nick McKenna | Mark Steedman
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)

Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence. Modality is commonly used in the political news domain, where both actual and possible courses of events are discussed. NLP systems struggle with these semantic phenomena, often incorrectly extracting events which did not happen, which can lead to issues in downstream applications. We present an open-domain, lexicon-based event extraction system that captures various types of modality. This information is valuable for Question Answering, Knowledge Graph construction and Fact-checking tasks, and our evaluation shows that the system is sufficiently strong to be used in downstream applications.

2020

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Aspectuality Across Genre: A Distributional Semantics Approach
Thomas Kober | Malihe Alikhani | Matthew Stone | Mark Steedman
Proceedings of the 28th International Conference on Computational Linguistics

The interpretation of the lexical aspect of verbs in English plays a crucial role in tasks such as recognizing textual entailment and learning discourse-level inferences. We show that two elementary dimensions of aspectual class, states vs. events, and telic vs. atelic events, can be modelled effectively with distributional semantics. We find that a verb’s local context is most indicative of its aspectual class, and we demonstrate that closed class words tend to be stronger discriminating contexts than content words. Our approach outperforms previous work on three datasets. Further, we present a new dataset of human-human conversations annotated with lexical aspects and present experiments that show the correlation of telicity with genre and discourse goals.

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The Role of Reentrancies in Abstract Meaning Representation Parsing
Ida Szubert | Marco Damonte | Shay B. Cohen | Mark Steedman
Findings of the Association for Computational Linguistics: EMNLP 2020

Abstract Meaning Representation (AMR) parsing aims at converting sentences into AMR representations. These are graphs and not trees because AMR supports reentrancies (nodes with more than one parent). Following previous findings on the importance of reen- trancies for AMR, we empirically find and discuss several linguistic phenomena respon- sible for reentrancies in AMR, some of which have not received attention before. We cate- gorize the types of errors AMR parsers make with respect to reentrancies. Furthermore, we find that correcting these errors provides an in- crease of up to 5% Smatch in parsing perfor- mance and 20% in reentrancy prediction

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Max-Margin Incremental CCG Parsing
Miloš Stanojević | Mark Steedman
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Incremental syntactic parsing has been an active research area both for cognitive scientists trying to model human sentence processing and for NLP researchers attempting to combine incremental parsing with language modelling for ASR and MT. Most effort has been directed at designing the right transition mechanism, but less has been done to answer the question of what a probabilistic model for those transition parsers should look like. A very incremental transition mechanism of a recently proposed CCG parser when trained in straightforward locally normalised discriminative fashion produces very bad results on English CCGbank. We identify three biases as the causes of this problem: label bias, exposure bias and imbalanced probabilities bias. While known techniques for tackling these biases improve results, they still do not make the parser state of the art. Instead, we tackle all of these three biases at the same time using an improved version of beam search optimisation that minimises all beam search violations instead of minimising only the biggest violation. The new incremental parser gives better results than all previously published incremental CCG parsers, and outperforms even some widely used non-incremental CCG parsers.

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The role of context in neural pitch accent detection in English
Elizabeth Nielsen | Mark Steedman | Sharon Goldwater
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Prosody is a rich information source in natural language, serving as a marker for phenomena such as contrast. In order to make this information available to downstream tasks, we need a way to detect prosodic events in speech. We propose a new model for pitch accent detection, inspired by the work of Stehwien et al. (2018), who presented a CNN-based model for this task. Our model makes greater use of context by using full utterances as input and adding an LSTM layer. We find that these innovations lead to an improvement from 87.5% to 88.7% accuracy on pitch accent detection on American English speech in the Boston University Radio News Corpus, a state-of-the-art result. We also find that a simple baseline that just predicts a pitch accent on every content word yields 82.2% accuracy, and we suggest that this is the appropriate baseline for this task. Finally, we conduct ablation tests that show pitch is the most important acoustic feature for this task and this corpus.

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Span-Based LCFRS-2 Parsing
Miloš Stanojević | Mark Steedman
Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies

The earliest models for discontinuous constituency parsers used mildly context-sensitive grammars, but the fashion has changed in recent years to grammar-less transition-based parsers that use strong neural probabilistic models to greedily predict transitions. We argue that grammar-based approaches still have something to contribute on top of what is offered by transition-based parsers. Concretely, by using a grammar formalism to restrict the space of possible trees we can use dynamic programming parsing algorithms for exact search for the most probable tree. Previous chart-based parsers for discontinuous formalisms used probabilistically weak generative models. We instead use a span-based discriminative neural model that preserves the dynamic programming properties of the chart parsers. Our parser does not use an explicit grammar, but it does use explicit grammar formalism constraints: we generate only trees that are within the LCFRS-2 formalism. These properties allow us to construct a new parsing algorithm that runs in lower worst-case time complexity of O(l nˆ4 +nˆ6), where n is the sentence length and l is the number of unique non-terminal labels. This parser is efficient in practice, provides best results among chart-based parsers, and is competitive with the best transition based parsers. We also show that the main bottleneck for further improvement in performance is in the restriction of fan-out to degree 2. We show that well-nestedness is helpful in speeding up parsing, but lowers accuracy.

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Incorporating Temporal Information in Entailment Graph Mining
Liane Guillou | Sander Bijl de Vroe | Mohammad Javad Hosseini | Mark Johnson | Mark Steedman
Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities. We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes. We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win ̸→ lose. We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.

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Learning Negation Scope from Syntactic Structure
Nick McKenna | Mark Steedman
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure. Linguistic theory posits that the semantics of negation can be understood purely syntactically, though recent research relies on combining a variety of features including part-of-speech tags, word embeddings, and semantic representations to achieve high task performance. Our simplified model returns to syntactic theory and achieves state-of-the-art performance on the task of Negation Scope Detection while demonstrating the tight relationship between the syntax and semantics of negation.

2019

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Node Embeddings for Graph Merging: Case of Knowledge Graph Construction
Ida Szubert | Mark Steedman
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

Combining two graphs requires merging the nodes which are counterparts of each other. In this process errors occur, resulting in incorrect merging or incorrect failure to merge. We find a high prevalence of such errors when using AskNET, an algorithm for building Knowledge Graphs from text corpora. AskNET node matching method uses string similarity, which we propose to replace with vector embedding similarity. We explore graph-based and word-based embedding models and show an overall error reduction of from 56% to 23.6%, with a reduction of over a half in both types of incorrect node matching.

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CCG Parsing Algorithm with Incremental Tree Rotation
Miloš Stanojević | Mark Steedman
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

The main obstacle to incremental sentence processing arises from right-branching constituent structures, which are present in the majority of English sentences, as well as optional constituents that adjoin on the right, such as right adjuncts and right conjuncts. In CCG, many right-branching derivations can be replaced by semantically equivalent left-branching incremental derivations. The problem of right-adjunction is more resistant to solution, and has been tackled in the past using revealing-based approaches that often rely either on the higher-order unification over lambda terms (Pareschi and Steedman,1987) or heuristics over dependency representations that do not cover the whole CCGbank (Ambati et al., 2015). We propose a new incremental parsing algorithm for CCG following the same revealing tradition of work but having a purely syntactic approach that does not depend on access to a distinct level of semantic representation. This algorithm can cover the whole CCGbank, with greater incrementality and accuracy than previous proposals.

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Temporal and Aspectual Entailment
Thomas Kober | Sander Bijl de Vroe | Mark Steedman
Proceedings of the 13th International Conference on Computational Semantics - Long Papers

Inferences regarding “Jane’s arrival in London” from predications such as “Jane is going to London” or “Jane has gone to London” depend on tense and aspect of the predications. Tense determines the temporal location of the predication in the past, present or future of the time of utterance. The aspectual auxiliaries on the other hand specify the internal constituency of the event, i.e. whether the event of “going to London” is completed and whether its consequences hold at that time or not. While tense and aspect are among the most important factors for determining natural language inference, there has been very little work to show whether modern embedding models capture these semantic concepts. In this paper we propose a novel entailment dataset and analyse the ability of contextualised word representations to perform inference on predications across aspectual types and tenses. We show that they encode a substantial amount of information relating to tense and aspect, but fail to consistently model inferences that require reasoning with these semantic properties.

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Construction and Alignment of Multilingual Entailment Graphs for Semantic Inference
Sabine Weber | Mark Steedman
Proceedings of the 2019 Workshop on Widening NLP

This paper presents ongoing work on the construction and alignment of predicate entailment graphs in English and German. We extract predicate-argument pairs from large corpora of monolingual English and German news text and construct monolingual paraphrase clusters and entailment graphs. We use an aligned subset of entities to derive the bilingual alignment of entities and relations, and achieve better than baseline results on a translated subset of a predicate entailment data set (Levy and Dagan, 2016) and the German portion of XNLI (Conneau et al., 2018).

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Wide-Coverage Neural A* Parsing for Minimalist Grammars
John Torr | Miloš Stanojević | Mark Steedman | Shay B. Cohen
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky’s (1995) Minimalist Program. This paper presents the first ever application of this formalism to the task of realistic wide-coverage parsing. The parser uses a linguistically expressive yet highly constrained grammar, together with an adaptation of the A* search algorithm currently used in CCG parsing (Lewis and Steedman, 2014; Lewis et al., 2016), with supertag probabilities provided by a bi-LSTM neural network supertagger trained on MGbank, a corpus of MG derivation trees. We report on some promising initial experimental results for overall dependency recovery as well as on the recovery of certain unbounded long distance dependencies. Finally, although like other MG parsers, ours has a high order polynomial worst case time complexity, we show that in practice its expected time complexity is cubic in the length of the sentence. The parser is publicly available.

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Duality of Link Prediction and Entailment Graph Induction
Mohammad Javad Hosseini | Shay B. Cohen | Mark Johnson | Mark Steedman
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Link prediction and entailment graph induction are often treated as different problems. In this paper, we show that these two problems are actually complementary. We train a link prediction model on a knowledge graph of assertions extracted from raw text. We propose an entailment score that exploits the new facts discovered by the link prediction model, and then form entailment graphs between relations. We further use the learned entailments to predict improved link prediction scores. Our results show that the two tasks can benefit from each other. The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.

2018

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The Lost Combinator
Mark Steedman
Computational Linguistics, Volume 44, Issue 4 - December 2018

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Character-Level Models versus Morphology in Semantic Role Labeling
Gözde Gül Şahin | Mark Steedman
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data. However, little is known on their ability to reveal the underlying morphological structure of a word, which is a crucial skill for high-level semantic analysis tasks, such as semantic role labeling (SRL). In this work, we train various types of SRL models that use word, character and morphology level information and analyze how performance of characters compare to words and morphology for several languages. We conduct an in-depth error analysis for each morphological typology and analyze the strengths and limitations of character-level models that relate to out-of-domain data, training data size, long range dependencies and model complexity. Our exhaustive analyses shed light on important characteristics of character-level models and their semantic capability.

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Predicting accuracy on large datasets from smaller pilot data
Mark Johnson | Peter Anderson | Mark Dras | Mark Steedman
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Because obtaining training data is often the most difficult part of an NLP or ML project, we develop methods for predicting how much data is required to achieve a desired test accuracy by extrapolating results from models trained on a small pilot training dataset. We model how accuracy varies as a function of training size on subsets of the pilot data, and use that model to predict how much training data would be required to achieve the desired accuracy. We introduce a new performance extrapolation task to evaluate how well different extrapolations predict accuracy on larger training sets. We show that details of hyperparameter optimisation and the extrapolation models can have dramatic effects in a document classification task. We believe this is an important first step in developing methods for estimating the resources required to meet specific engineering performance targets.

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Learning Typed Entailment Graphs with Global Soft Constraints
Mohammad Javad Hosseini | Nathanael Chambers | Siva Reddy | Xavier R. Holt | Shay B. Cohen | Mark Johnson | Mark Steedman
Transactions of the Association for Computational Linguistics, Volume 6

This paper presents a new method for learning typed entailment graphs from text. We extract predicate-argument structures from multiple-source news corpora, and compute local distributional similarity scores to learn entailments between predicates with typed arguments (e.g., person contracted disease). Previous work has used transitivity constraints to improve local decisions, but these constraints are intractable on large graphs. We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph. Learning takes only a few hours to run over 100K predicates and our results show large improvements over local similarity scores on two entailment data sets. We further show improvements over paraphrases and entailments from the Paraphrase Database, and prior state-of-the-art entailment graphs. We show that the entailment graphs improve performance in a downstream task.

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Data Augmentation via Dependency Tree Morphing for Low-Resource Languages
Gözde Gül Şahin | Mark Steedman
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Neural NLP systems achieve high scores in the presence of sizable training dataset. Lack of such datasets leads to poor system performances in the case low-resource languages. We present two simple text augmentation techniques using dependency trees, inspired from image processing. We “crop” sentences by removing dependency links, and we “rotate” sentences by moving the tree fragments around the root. We apply these techniques to augment the training sets of low-resource languages in Universal Dependencies project. We implement a character-level sequence tagging model and evaluate the augmented datasets on part-of-speech tagging task. We show that crop and rotate provides improvements over the models trained with non-augmented data for majority of the languages, especially for languages with rich case marking systems.

2017

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Universal Semantic Parsing
Siva Reddy | Oscar Täckström | Slav Petrov | Mark Steedman | Mirella Lapata
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process dependency graphs, which allow handling complex phenomena such as control. In this work, we introduce UDepLambda, a semantic interface for UD, which maps natural language to logical forms in an almost language-independent fashion and can process dependency graphs. We perform experiments on question answering against Freebase and provide German and Spanish translations of the WebQuestions and GraphQuestions datasets to facilitate multilingual evaluation. Results show that UDepLambda outperforms strong baselines across languages and datasets. For English, it achieves a 4.9 F1 point improvement over the state-of-the-art on GraphQuestions.

2016

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Evaluating Induced CCG Parsers on Grounded Semantic Parsing
Yonatan Bisk | Siva Reddy | John Blitzer | Julia Hockenmaier | Mark Steedman
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Shift-Reduce CCG Parsing using Neural Network Models
Bharat Ram Ambati | Tejaswini Deoskar | Mark Steedman
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Assessing Relative Sentence Complexity using an Incremental CCG Parser
Bharat Ram Ambati | Siva Reddy | Mark Steedman
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Transforming Dependency Structures to Logical Forms for Semantic Parsing
Siva Reddy | Oscar Täckström | Michael Collins | Tom Kwiatkowski | Dipanjan Das | Mark Steedman | Mirella Lapata
Transactions of the Association for Computational Linguistics, Volume 4

The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to the lack of a strong type system—dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.

2015

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Parser Adaptation to the Biomedical Domain without Re-Training
Jeff Mitchell | Mark Steedman
Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

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Orthogonality of Syntax and Semantics within Distributional Spaces
Jeff Mitchell | Mark Steedman
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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A Computationally Efficient Algorithm for Learning Topical Collocation Models
Zhendong Zhao | Lan Du | Benjamin Börschinger | John K Pate | Massimiliano Ciaramita | Mark Steedman | Mark Johnson
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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An Incremental Algorithm for Transition-based CCG Parsing
Bharat Ram Ambati | Tejaswini Deoskar | Mark Johnson | Mark Steedman
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Lexical Event Ordering with an Edge-Factored Model
Omri Abend | Shay B. Cohen | Mark Steedman
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Generalizing a Strongly Lexicalized Parser using Unlabeled Data
Tejaswini Deoskar | Christos Christodoulopoulos | Alexandra Birch | Mark Steedman
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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A Generative Model for User Simulation in a Spatial Navigation Domain
Aciel Eshky | Ben Allison | Subramanian Ramamoorthy | Mark Steedman
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Improving Dependency Parsers using Combinatory Categorial Grammar
Bharat Ram Ambati | Tejaswini Deoskar | Mark Steedman
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

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Improved CCG Parsing with Semi-supervised Supertagging
Mike Lewis | Mark Steedman
Transactions of the Association for Computational Linguistics, Volume 2

Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal. We show how a state-of-the-art CCG parser can be enhanced, by predicting lexical categories using unsupervised vector-space embeddings of words. The use of word embeddings enables our model to better generalize from the labelled data, and allows us to accurately assign lexical categories without depending on a POS-tagger. Our approach leads to substantial improvements in dependency parsing results over the standard supervised CCG parser when evaluated on Wall Street Journal (0.8%), Wikipedia (1.8%) and biomedical (3.4%) text. We compare the performance of two recently proposed approaches for classification using a wide variety of word embeddings. We also give a detailed error analysis demonstrating where using embeddings outperforms traditional feature sets, and showing how including POS features can decrease accuracy.

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Large-scale Semantic Parsing without Question-Answer Pairs
Siva Reddy | Mirella Lapata | Mark Steedman
Transactions of the Association for Computational Linguistics, Volume 2

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs. Our key insight is to represent natural language via semantic graphs whose topology shares many commonalities with Freebase. Given this representation, we conceptualize semantic parsing as a graph matching problem. Our model converts sentences to semantic graphs using CCG and subsequently grounds them to Freebase guided by denotations as a form of weak supervision. Evaluation experiments on a subset of the Free917 and WebQuestions benchmark datasets show our semantic parser improves over the state of the art.

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Robust Semantics for Semantic Parsing
Mark Steedman
Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing

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Combining Formal and Distributional Models of Temporal and Intensional Semantics
Mike Lewis | Mark Steedman
Proceedings of the ACL 2014 Workshop on Semantic Parsing

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A* CCG Parsing with a Supertag-factored Model
Mike Lewis | Mark Steedman
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Lexical Inference over Multi-Word Predicates: A Distributional Approach
Omri Abend | Shay B. Cohen | Mark Steedman
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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Unsupervised Induction of Cross-Lingual Semantic Relations
Mike Lewis | Mark Steedman
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Robust Computational Semantics
Mark Steedman
Proceedings of the Australasian Language Technology Association Workshop 2013 (ALTA 2013)

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Combined Distributional and Logical Semantics
Mike Lewis | Mark Steedman
Transactions of the Association for Computational Linguistics, Volume 1

We introduce a new approach to semantics which combines the benefits of distributional and formal logical semantics. Distributional models have been successful in modelling the meanings of content words, but logical semantics is necessary to adequately represent many function words. We follow formal semantics in mapping language to logical representations, but differ in that the relational constants used are induced by offline distributional clustering at the level of predicate-argument structure. Our clustering algorithm is highly scalable, allowing us to run on corpora the size of Gigaword. Different senses of a word are disambiguated based on their induced types. We outperform a variety of existing approaches on a wide-coverage question answering task, and demonstrate the ability to make complex multi-sentence inferences involving quantifiers on the FraCaS suite.

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Using CCG categories to improve Hindi dependency parsing
Bharat Ram Ambati | Tejaswini Deoskar | Mark Steedman
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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The Effect of Higher-Order Dependency Features in Discriminative Phrase-Structure Parsing
Greg Coppola | Mark Steedman
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2012

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Generative Goal-Driven User Simulation for Dialog Management
Aciel Eshky | Ben Allison | Mark Steedman
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Probabilistic Models of Grammar Acquisition
Mark Steedman
Proceedings of the Workshop on Computational Models of Language Acquisition and Loss

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Turning the pipeline into a loop: Iterated unsupervised dependency parsing and PoS induction
Christos Christodoulopoulos | Sharon Goldwater | Mark Steedman
Proceedings of the NAACL-HLT Workshop on the Induction of Linguistic Structure

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A Probabilistic Model of Syntactic and Semantic Acquisition from Child-Directed Utterances and their Meanings
Tom Kwiatkowski | Sharon Goldwater | Luke Zettlemoyer | Mark Steedman
Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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Computing Scope in a CCG Parser
Mark Steedman
Proceedings of the 12th International Conference on Parsing Technologies

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Simple Semi-Supervised Learning for Prepositional Phrase Attachment
Gregory F. Coppola | Alexandra Birch | Tejaswini Deoskar | Mark Steedman
Proceedings of the 12th International Conference on Parsing Technologies

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A Bayesian Mixture Model for PoS Induction Using Multiple Features
Christos Christodoulopoulos | Sharon Goldwater | Mark Steedman
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Semi-supervised CCG Lexicon Extension
Emily Thomforde | Mark Steedman
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Lexical Generalization in CCG Grammar Induction for Semantic Parsing
Tom Kwiatkowski | Luke Zettlemoyer | Sharon Goldwater | Mark Steedman
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

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Grammar Induction from Text Using Small Syntactic Prototypes
Prachya Boonkwan | Mark Steedman
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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A Multi-Dimensional Analysis of Japanese Benefactives: The Case of the Yaru-Construction
Akira Otani | Mark Steedman
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation

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Two Decades of Unsupervised POS Induction: How Far Have We Come?
Christos Christodoulopoulos | Sharon Goldwater | Mark Steedman
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

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Inducing Probabilistic CCG Grammars from Logical Form with Higher-Order Unification
Tom Kwiatkowksi | Luke Zettlemoyer | Sharon Goldwater | Mark Steedman
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Unbounded Dependency Recovery for Parser Evaluation
Laura Rimell | Stephen Clark | Mark Steedman
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Note on Japanese Epistemic Verb Constructions: A Surface-Compositional Analysis
Akira Ohtani | Mark Steedman
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1

2008

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Last Words: On Becoming a Discipline
Mark Steedman
Computational Linguistics, Volume 34, Number 1, March 2008

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On Japanese Desiderative Constructions
Akira Ohtani | Mark Steedman
Proceedings of the 22nd Pacific Asia Conference on Language, Information and Computation

2007

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Planning Dialog Actions
Mark Steedman | Ronald Petrick
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

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CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Julia Hockenmaier | Mark Steedman
Computational Linguistics, Volume 33, Number 3, September 2007

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Case, Coordination, and Information Structure in Japanese
Akira Otani | Mark Steedman
Proceedings of the 21st Pacific Asia Conference on Language, Information and Computation

2005

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A Framework for Annotating Information Structure in Discourse
Sasha Calhoun | Malvina Nissim | Mark Steedman | Jason Brenier
Proceedings of the Workshop on Frontiers in Corpus Annotations II: Pie in the Sky

2004

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Object-Extraction and Question-Parsing using CCG
Stephen Clark | Mark Steedman | James R. Curran
Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing

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An Annotation Scheme for Information Status in Dialogue
Malvina Nissim | Shipra Dingare | Jean Carletta | Mark Steedman
Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04)

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Wide-Coverage Semantic Representations from a CCG Parser
Johan Bos | Stephen Clark | Mark Steedman | James R. Curran | Julia Hockenmaier
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2003

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Example Selection for Bootstrapping Statistical Parsers
Mark Steedman | Rebecca Hwa | Stephen Clark | Miles Osborne | Anoop Sarkar | Julia Hockenmaier | Paul Ruhlen | Steven Baker | Jeremiah Crim
Proceedings of the 2003 Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics

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Bootstrapping statistical parsers from small datasets
Mark Steedman | Miles Osborne | Anoop Sarkar | Stephen Clark | Rebecca Hwa | Julia Hockenmaier | Paul Ruhlen | Steven Baker | Jeremiah Crim
10th Conference of the European Chapter of the Association for Computational Linguistics

2002

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Acquiring Compact Lexicalized Grammars from a Cleaner Treebank
Julia Hockenmaier | Mark Steedman
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)

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Building Deep Dependency Structures using a Wide-Coverage CCG Parser
Stephen Clark | Julia Hockenmaier | Mark Steedman
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

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Generative Models for Statistical Parsing with Combinatory Categorial Grammar
Julia Hockenmaier | Mark Steedman
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

1999

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Alternating Quantifier Scope in CCG
Mark Steedman
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics

1997

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Making Use of Intonation in Interactive Dialogue Translation
Mark Steedman
Proceedings of the Fifth International Workshop on Parsing Technologies

Intonational information is frequently discarded in speech recognition, and assigned by default heuristics in text-to-speech generation. However, in many applications involving dialogue and interactive discourse, intonation conveys significant information, and we ignore it at our peril. Translating telephones and personal assistants are an interesting test case, in which the salience of rapidly shifting discourse topics and the fact that sentences are machine-generated, rather than written by humans, combine to make the application particularly vulnerable to our poor theoretical grasp of intonation and its functions. I will discuss a number of approaches to the problem for such applications, ranging from cheap tricks to a combinatory grammar-based theory of the semantics involved and a syntax-phonology interface for building and generating from interpretations.

1994

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Information Based Intonation Synthesis
Scott Prevost | Mark Steedman
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

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Research in Natural Language Processing
A. Joshi | M. Marcus | M. Steedman | B. Webber
Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994

1993

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Natural Language Research
Aravind Joshi | Mitch Marcus | Mark Steedman | Bonnie Webber
Human Language Technology: Proceedings of a Workshop Held at Plainsboro, New Jersey, March 21-24, 1993

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Generating Contextually Appropriate Intonation
Scott Prevost | Mark Steedman
Sixth Conference of the European Chapter of the Association for Computational Linguistics

1992

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Natural Language Research
Aravind Joshi | Mitch Marcus | Mark Steedman | Bonnie Webber
Speech and Natural Language: Proceedings of a Workshop Held at Harriman, New York, February 23-26, 1992

1991

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Type-Raising and Directionality in Combinatory Grammar
Mark Steedman
29th Annual Meeting of the Association for Computational Linguistics

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Natural Language Research
Aravind K. Joshi | Mitch Marcus | Mark Steedman | Bonnie Webber
Speech and Natural Language: Proceedings of a Workshop Held at Pacific Grove, California, February 19-22, 1991

1990

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Structure and Intonation in Spoken Language Understanding
Mark Steedman
28th Annual Meeting of the Association for Computational Linguistics

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Natural Language Research
Aravind Joshi | Mitch Marcus | Mark Steedman | Bonnie Webber
Speech and Natural Language: Proceedings of a Workshop Held at Hidden Valley, Pennsylvania, June 24-27,1990

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Narrated Animation: A Case for Generation
Norman Badler | Mark Steedman | Bonnie Lynn Webber
Proceedings of the Fifth International Workshop on Natural Language Generation

1989

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Natural Language Research
Aravind Joshi | Mitch Marcus | Mark Steedman | Bonnie Webber
Speech and Natural Language: Proceedings of a Workshop Held at Philadelphia, Pennsylvania, February 21-23, 1989

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Intonation and Syntax in Spoken Language Systems
Mark Steedman
Speech and Natural Language: Proceedings of a Workshop Held at Philadelphia, Pennsylvania, February 21-23, 1989

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Parsing Spoken Language Using Combinatory Grammars
Mark Steedman
Proceedings of the First International Workshop on Parsing Technologies

1988

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Temporal Ontology and Temporal Reference
Marc Moens | Mark Steedman
Computational Linguistics, Volume 14, Number 2, June 1988

1987

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Temporal Ontology in Natural Language
Marc Moens | Mark Steedman
25th Annual Meeting of the Association for Computational Linguistics

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A Lazy way to Chart-Parse with Categorial Grammars
Remo Pareschi | Mark Steedman
25th Annual Meeting of the Association for Computational Linguistics