Search Results for author: Mark Steedman

Found 51 papers, 12 papers with code

Blindness to Modality Helps Entailment Graph Mining

1 code implementation21 Sep 2021 Liane Guillou, Sander Bijl de Vroe, Mark Johnson, Mark Steedman

Understanding linguistic modality is widely seen as important for downstream tasks such as Question Answering and Knowledge Graph Population.

Modality and Negation in Event Extraction

1 code implementation20 Sep 2021 Sander Bijl de Vroe, Liane Guillou, Miloš Stanojević, Nick McKenna, Mark Steedman

Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence.

Event Extraction Fact Checking +2

Incorporating Temporal Information in Entailment Graph Mining

1 code implementation20 Sep 2021 Liane Guillou, Sander Bijl de Vroe, Mohammad Javad Hosseini, Mark Johnson, Mark Steedman

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.

Graph Mining

Prosodic segmentation for parsing spoken dialogue

no code implementations ACL 2021 Elizabeth Nielsen, Mark Steedman, Sharon Goldwater

We investigate how prosody affects a parser that receives an entire dialogue turn as input (a turn-based model), instead of gold standard pre-segmented SUs (an SU-based model).

Multivalent Entailment Graphs for Question Answering

no code implementations16 Apr 2021 Nick McKenna, Liane Guillou, Mohammad Javad Hosseini, Sander Bijl de Vroe, Mark Johnson, Mark Steedman

Drawing inferences between open-domain natural language predicates is a necessity for true language understanding.

Question Answering

Learning Negation Scope from Syntactic Structure

no code implementations Joint Conference on Lexical and Computational Semantics 2020 Nick McKenna, Mark Steedman

We present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure.

Word Embeddings

Aspectuality Across Genre: A Distributional Semantics Approach

no code implementations COLING 2020 Thomas Kober, Malihe Alikhani, Matthew Stone, Mark Steedman

The interpretation of the lexical aspect of verbs in English plays a crucial role for recognizing textual entailment and learning discourse-level inferences.

Natural Language Inference

Span-Based LCFRS-2 Parsing

no code implementations WS 2020 Milo{\v{s}} Stanojevi{\'c}, Mark Steedman

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.

Max-Margin Incremental CCG Parsing

no code implementations ACL 2020 Milo{\v{s}} Stanojevi{\'c}, Mark Steedman

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.

Language Modelling

The role of context in neural pitch accent detection in English

no code implementations EMNLP 2020 Elizabeth Nielsen, Mark Steedman, Sharon Goldwater

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.

Construction and Alignment of Multilingual Entailment Graphs for Semantic Inference

no code implementations WS 2019 Sabine Weber, Mark Steedman

This paper presents ongoing work on the construction and alignment of predicate entailment graphs in English and German.

Duality of Link Prediction and Entailment Graph Induction

1 code implementation ACL 2019 Mohammad Javad Hosseini, Shay B. Cohen, Mark Johnson, Mark Steedman

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.

Link Prediction

Wide-Coverage Neural A* Parsing for Minimalist Grammars

no code implementations ACL 2019 John Torr, Milos Stanojevic, Mark Steedman, Shay B. Cohen

Minimalist Grammars (Stabler, 1997) are a computationally oriented, and rigorous formalisation of many aspects of Chomsky{'}s (1995) Minimalist Program.

CCG Parsing Algorithm with Incremental Tree Rotation

1 code implementation NAACL 2019 Milo{\v{s}} Stanojevi{\'c}, Mark Steedman

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.

Temporal and Aspectual Entailment

1 code implementation WS 2019 Thomas Kober, Sander Bijl de Vroe, Mark Steedman

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.

Natural Language Inference

Predicting accuracy on large datasets from smaller pilot data

no code implementations ACL 2018 Mark Johnson, Peter Anderson, Mark Dras, Mark Steedman

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.

Document Classification

Character-Level Models versus Morphology in Semantic Role Labeling

1 code implementation ACL 2018 Gözde Gül Şahin, Mark Steedman

Character-level models have become a popular approach specially for their accessibility and ability to handle unseen data.

Semantic Role Labeling

Learning Typed Entailment Graphs with Global Soft Constraints

1 code implementation TACL 2018 Mohammad Javad Hosseini, Nathanael Chambers, Siva Reddy, Xavier R. Holt, Shay B. Cohen, Mark Johnson, Mark Steedman

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.

Graph Learning

Universal Semantic Parsing

1 code implementation EMNLP 2017 Siva Reddy, Oscar Täckström, Slav Petrov, Mark Steedman, Mirella Lapata

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.

Question Answering Semantic Parsing

Evaluating Induced CCG Parsers on Grounded Semantic Parsing

1 code implementation EMNLP 2016 Yonatan Bisk, Siva Reddy, John Blitzer, Julia Hockenmaier, Mark Steedman

We compare the effectiveness of four different syntactic CCG parsers for a semantic slot-filling task to explore how much syntactic supervision is required for downstream semantic analysis.

Semantic Parsing Slot Filling

Transforming Dependency Structures to Logical Forms for Semantic Parsing

1 code implementation TACL 2016 Siva Reddy, Oscar T{\"a}ckstr{\"o}m, Michael Collins, Tom Kwiatkowski, Dipanjan Das, Mark Steedman, Mirella Lapata

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.

Question Answering Semantic Parsing

Improved CCG Parsing with Semi-supervised Supertagging

no code implementations TACL 2014 Mike Lewis, Mark Steedman

Current supervised parsers are limited by the size of their labelled training data, making improving them with unlabelled data an important goal.

CCG Supertagging Dependency Parsing +5

Large-scale Semantic Parsing without Question-Answer Pairs

no code implementations TACL 2014 Siva Reddy, Mirella Lapata, Mark Steedman

In this paper we introduce a novel semantic parsing approach to query Freebase in natural language without requiring manual annotations or question-answer pairs.

Graph Matching Semantic Parsing

Combined Distributional and Logical Semantics

no code implementations TACL 2013 Mike Lewis, Mark Steedman

We introduce a new approach to semantics which combines the benefits of distributional and formal logical semantics.

Question Answering Relation Extraction

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