Search Results for author: Matthew Purver

Found 38 papers, 7 papers with code

Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis

no code implementations SIGDIAL (ACL) 2021 Shamila Nasreen, Julian Hough, Matthew Purver

Alzheimer’s Disease (AD) is associated with many characteristic changes, not only in an individual’s language but also in the interactive patterns observed in dialogue.

Communicative Grounding of Analogical Explanations in Dialogue: A Corpus Study of Conversational Management Acts and Statistical Sequence Models for Tutoring through Analogy

no code implementations ReInAct 2021 Jorge Del-Bosque-Trevino, Julian Hough, Matthew Purver

We annotate a corpus of analogical episodes with the schema and develop statistical sequence models from the corpus which predict tutor content related decisions, in terms of the selection of the analogical component (AC) and tutor conversational management act (TCMA) to deploy at the current utterance, given the student’s behaviour.

Measuring and Improving Compositional Generalization in Text-to-SQL via Component Alignment

no code implementations4 May 2022 Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver

To deal with this problem, we modify a number of state-of-the-art models to train on the segmented data of Spider-SS, and we show that this method improves the generalization performance.

Text-To-Sql

Natural SQL: Making SQL Easier to Infer from Natural Language Specifications

1 code implementation Findings (EMNLP) 2021 Yujian Gan, Xinyun Chen, Jinxia Xie, Matthew Purver, John R. Woodward, John Drake, Qiaofu Zhang

Addressing the mismatch between natural language descriptions and the corresponding SQL queries is a key challenge for text-to-SQL translation.

Text-To-Sql Translation

Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization

1 code implementation EMNLP 2021 Yujian Gan, Xinyun Chen, Matthew Purver

Recently, there has been significant progress in studying neural networks for translating text descriptions into SQL queries under the zero-shot cross-domain setting.

Text-To-Sql Translation

A Longitudinal Multi-modal Dataset for Dementia Monitoring and Diagnosis

no code implementations3 Sep 2021 Dimitris Gkoumas, Bo wang, Adam Tsakalidis, Maria Wolters, Arkaitz Zubiaga, Matthew Purver, Maria Liakata

Dementia is a family of neurogenerative conditions affecting memory and cognition in an increasing number of individuals in our globally aging population.

Evaluation of contextual embeddings on less-resourced languages

no code implementations22 Jul 2021 Matej Ulčar, Aleš Žagar, Carlos S. Armendariz, Andraž Repar, Senja Pollak, Matthew Purver, Marko Robnik-Šikonja

The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives.

Dependency Parsing

Alzheimer's Dementia Recognition Using Acoustic, Lexical, Disfluency and Speech Pause Features Robust to Noisy Inputs

no code implementations29 Jun 2021 Morteza Rohanian, Julian Hough, Matthew Purver

We present two multimodal fusion-based deep learning models that consume ASR transcribed speech and acoustic data simultaneously to classify whether a speaker in a structured diagnostic task has Alzheimer's Disease and to what degree, evaluating the ADReSSo challenge 2021 data.

Multi-modal fusion with gating using audio, lexical and disfluency features for Alzheimer's Dementia recognition from spontaneous speech

1 code implementation17 Jun 2021 Morteza Rohanian, Julian Hough, Matthew Purver

This paper is a submission to the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge, which aims to develop methods that can assist in the automated prediction of severity of Alzheimer's Disease from speech data.

Towards Robustness of Text-to-SQL Models against Synonym Substitution

1 code implementation ACL 2021 Yujian Gan, Xinyun Chen, Qiuping Huang, Matthew Purver, John R. Woodward, Jinxia Xie, Pengsheng Huang

We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks.

Text-To-Sql

SemEval-2020 Task 3: Graded Word Similarity in Context

no code implementations SEMEVAL 2020 Carlos Santos Armendariz, Matthew Purver, Senja Pollak, Nikola Ljube{\v{s}}i{\'c}, Matej Ul{\v{c}}ar, Ivan Vuli{\'c}, Mohammad Taher Pilehvar

This paper presents the Graded Word Similarity in Context (GWSC) task which asked participants to predict the effects of context on human perception of similarity in English, Croatian, Slovene and Finnish.

Translation Word Similarity

Exploring Semantic Incrementality with Dynamic Syntax and Vector Space Semantics

no code implementations1 Nov 2018 Mehrnoosh Sadrzadeh, Matthew Purver, Julian Hough, Ruth Kempson

One of the fundamental requirements for models of semantic processing in dialogue is incrementality: a model must reflect how people interpret and generate language at least on a word-by-word basis, and handle phenomena such as fragments, incomplete and jointly-produced utterances.

Incongruent Headlines: Yet Another Way to Mislead Your Readers

no code implementations WS 2017 Sophie Chesney, Maria Liakata, Massimo Poesio, Matthew Purver

This paper discusses the problem of incongruent headlines: those which do not accurately represent the information contained in the article with which they occur.

Words, Concepts, and the Geometry of Analogy

no code implementations4 Aug 2016 Stephen McGregor, Matthew Purver, Geraint Wiggins

This paper presents a geometric approach to the problem of modelling the relationship between words and concepts, focusing in particular on analogical phenomena in language and cognition.

Strongly Incremental Repair Detection

no code implementations EMNLP 2014 Julian Hough, Matthew Purver

We present STIR (STrongly Incremental Repair detection), a system that detects speech repairs and edit terms on transcripts incrementally with minimal latency.

Evaluating Neural Word Representations in Tensor-Based Compositional Settings

no code implementations EMNLP 2014 Dmitrijs Milajevs, Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, Matthew Purver

We provide a comparative study between neural word representations and traditional vector spaces based on co-occurrence counts, in a number of compositional tasks.

Sentence Similarity Word Embeddings

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