Search Results for author: Mark Steedman

Found 74 papers, 27 papers with code

Semi-Automatic Construction of Text-to-SQL Data for Domain Transfer

1 code implementation ACL (IWPT) 2021 Tianyi Li, Sujian Li, Mark Steedman

Strong and affordable in-domain data is a desirable asset when transferring trained semantic parsers to novel domains.


Computing All Quantifier Scopes with CCG

1 code implementation IWCS (ACL) 2021 Miloš Stanojević, Mark Steedman

We present a method for computing all quantifer scopes that can be extracted from a single CCG derivation.

Modeling Incremental Language Comprehension in the Brain with Combinatory Categorial Grammar

no code implementations NAACL (CMCL) 2021 Miloš Stanojević, Shohini Bhattasali, Donald Dunagan, Luca Campanelli, Mark Steedman, Jonathan Brennan, John Hale

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.


Fine-grained General Entity Typing in German using GermaNet

1 code implementation NAACL (TextGraphs) 2021 Sabine Weber, Mark Steedman

We use a German WordNet equivalent, GermaNet, to automatically generate training data for German general entity typing.

Entity Typing Relation Extraction +1

Formal Basis of a Language Universal

no code implementations CL (ACL) 2021 Miloš Stanojević, Mark Steedman

Abstract Steedman (2020) proposes as a formal universal of natural language grammar that grammatical permutations of the kind that have given rise to transformational rules are limited to a class known to mathematicians and computer scientists as the “separable” permutations.

Machine Translation

Zero-Shot Cross-Lingual Transfer is a Hard Baseline to Beat in German Fine-Grained Entity Typing

no code implementations EMNLP (insights) 2021 Sabine Weber, Mark Steedman

The training of NLP models often requires large amounts of labelled training data, which makes it difficult to expand existing models to new languages.

Entity Typing Multilingual Word Embeddings +4

Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs

1 code implementation Findings (EMNLP) 2021 Mohammad Javad Hosseini, Shay B. Cohen, Mark Johnson, Mark Steedman

In this paper, we introduce the new task of open-domain contextual link prediction which has access to both the textual context and the KG structure to perform link prediction.

Link Prediction

A Usage-centric Take on Intent Understanding in E-Commerce

no code implementations22 Feb 2024 Wendi Zhou, Tianyi Li, Pavlos Vougiouklis, Mark Steedman, Jeff Z. Pan

Identifying and understanding user intents is a pivotal task for E-Commerce.

Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets

1 code implementation29 Jan 2024 Nikita Moghe, Arnisa Fazla, Chantal Amrhein, Tom Kocmi, Mark Steedman, Alexandra Birch, Rico Sennrich, Liane Guillou

We benchmark metric performance, assess their incremental performance over successive campaigns, and measure their sensitivity to a range of linguistic phenomena.

Benchmarking Machine Translation +3

Sentence-Incremental Neural Coreference Resolution

1 code implementation26 May 2023 Matt Grenander, Shay B. Cohen, Mark Steedman

We propose a sentence-incremental neural coreference resolution system which incrementally builds clusters after marking mention boundaries in a shift-reduce method.

coreference-resolution Sentence

Sources of Hallucination by Large Language Models on Inference Tasks

1 code implementation23 May 2023 Nick McKenna, Tianyi Li, Liang Cheng, Mohammad Javad Hosseini, Mark Johnson, Mark Steedman

Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization.

Hallucination Memorization +2

Prosodic features improve sentence segmentation and parsing

1 code implementation23 Feb 2023 Elizabeth Nielsen, Sharon Goldwater, Mark Steedman

Parsing spoken dialogue presents challenges that parsing text does not, including a lack of clear sentence boundaries.

Sentence Sentence segmentation

Extrinsic Evaluation of Machine Translation Metrics

no code implementations20 Dec 2022 Nikita Moghe, Tom Sherborne, Mark Steedman, Alexandra Birch

We calculate the correlation between the metric's ability to predict a good/bad translation with the success/failure on the final task for the Translate-Test setup.

Dialogue State Tracking Machine Translation +4

Language Models Are Poor Learners of Directional Inference

1 code implementation10 Oct 2022 Tianyi Li, Mohammad Javad Hosseini, Sabine Weber, Mark Steedman

We examine LMs' competence of directional predicate entailments by supervised fine-tuning with prompts.

Multi-Document Summarization with Centroid-Based Pretraining

1 code implementation1 Aug 2022 Ratish Puduppully, Parag Jain, Nancy F. Chen, Mark Steedman

In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary.

Document Summarization Multi-Document Summarization

Smoothing Entailment Graphs with Language Models

1 code implementation30 Jul 2022 Nick McKenna, Tianyi Li, Mark Johnson, Mark Steedman

The diversity and Zipfian frequency distribution of natural language predicates in corpora leads to sparsity in Entailment Graphs (EGs) built by Open Relation Extraction (ORE).

Explainable Models Language Modelling +2

Zero-shot Cross-Linguistic Learning of Event Semantics

no code implementations5 Jul 2022 Malihe Alikhani, Thomas Kober, Bashar Alhafni, Yue Chen, Mert Inan, Elizabeth Nielsen, Shahab Raji, Mark Steedman, Matthew Stone

Typologically diverse languages offer systems of lexical and grammatical aspect that allow speakers to focus on facets of event structure in ways that comport with the specific communicative setting and discourse constraints they face.

Cross-lingual Inference with A Chinese Entailment Graph

1 code implementation Findings (ACL) 2022 Tianyi Li, Sabine Weber, Mohammad Javad Hosseini, Liane Guillou, Mark Steedman

Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relation triples.

Entity Typing Question Answering +2

Cross-lingual Intermediate Fine-tuning improves Dialogue State Tracking

1 code implementation EMNLP 2021 Nikita Moghe, Mark Steedman, Alexandra Birch

In this work, we enhance the transfer learning process by intermediate fine-tuning of pretrained multilingual models, where the multilingual models are fine-tuned with different but related data and/or tasks.

Cross-Lingual Transfer Dialogue State Tracking +2

Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech

2 code implementations22 Sep 2021 Ida Szubert, Omri Abend, Nathan Schneider, Samuel Gibbon, Louis Mahon, Sharon Goldwater, Mark Steedman

We then demonstrate the utility of the compiled corpora through (1) a longitudinal corpus study of the prevalence of different syntactic and semantic phenomena in the CDS, and (2) applying an existing computational model of language acquisition to the two corpora and briefly comparing the results across languages.

Language Acquisition Semantic Parsing

Blindness to Modality Helps Entailment Graph Mining

1 code implementation EMNLP (insights) 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.

Graph Learning Graph Mining +1

Incorporating Temporal Information in Entailment Graph Mining

1 code implementation COLING (TextGraphs) 2020 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

Modality and Negation in Event Extraction

1 code implementation ACL (CASE) 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 +3

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).

Segmentation Sentence

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

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 Sentence

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.


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.

Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction

no code implementations23 Aug 2019 Zhepei Wei, Yantao Jia, Yuan Tian, Mohammad Javad Hosseini, Sujian Li, Mark Steedman, Yi Chang

In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.

Entity Extraction using GAN graph construction +2

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 +1

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 +1

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

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

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.

Clustering Question Answering +2

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