Search Results for author: Ioannis Konstas

Found 35 papers, 17 papers with code

SPaR.txt, a cheap Shallow Parsing approach for Regulatory texts

1 code implementation4 Oct 2021 Ruben Kruiper, Ioannis Konstas, Alasdair Gray, Farhad Sadeghineko, Richard Watson, Bimal Kumar

Automated Compliance Checking (ACC) systems aim to semantically parse building regulations to a set of rules.

Semantic Parsing

MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization

1 code implementation22 Sep 2021 Xinnuo Xu, Ondřej Dušek, Shashi Narayan, Verena Rieser, Ioannis Konstas

We show via data analysis that it's not only the models which are to blame: more than 27% of facts mentioned in the gold summaries of MiRANews are better grounded on assisting documents than in the main source articles.

Document Summarization Multi-Document Summarization

AGGGEN: Ordering and Aggregating while Generating

1 code implementation ACL 2021 Xinnuo Xu, Ondřej Dušek, Verena Rieser, Ioannis Konstas

We present AGGGEN (pronounced 'again'), a data-to-text model which re-introduces two explicit sentence planning stages into neural data-to-text systems: input ordering and input aggregation.

Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games

no code implementations COLING 2020 Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon

However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time.

Findings of the Fourth Workshop on Neural Generation and Translation

no code implementations WS 2020 Kenneth Heafield, Hiroaki Hayashi, Yusuke Oda, Ioannis Konstas, Andrew Finch, Graham Neubig, Xi-An Li, Alex Birch, ra

We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020).

Document-level Machine Translation +1

Fact-based Content Weighting for Evaluating Abstractive Summarisation

no code implementations ACL 2020 Xinnuo Xu, Ond{\v{r}}ej Du{\v{s}}ek, Jingyi Li, Verena Rieser, Ioannis Konstas

Abstractive summarisation is notoriously hard to evaluate since standard word-overlap-based metrics are insufficient.

CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning

no code implementations ACL 2020 Alessandro Suglia, Ioannis Konstas, Andrea Vanzo, Emanuele Bastianelli, Desmond Elliott, Stella Frank, Oliver Lemon

To remedy this, we present GROLLA, an evaluation framework for Grounded Language Learning with Attributes with three sub-tasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zero-shot evaluation.

Grounded language learning

A Scientific Information Extraction Dataset for Nature Inspired Engineering

1 code implementation LREC 2020 Ruben Kruiper, Julian F. V. Vincent, Jessica Chen-Burger, Marc P. Y. Desmulliez, Ioannis Konstas

Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices.

Relation Extraction

In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts

1 code implementation ACL 2020 Ruben Kruiper, Julian F. V. Vincent, Jessica Chen-Burger, Marc P. Y. Desmulliez, Ioannis Konstas

First, we present the Focused Open Biological Information Extraction (FOBIE) dataset and use FOBIE to train a state-of-the-art narrow scientific IE system to extract trade-off relations and arguments that are central to biology texts.

Relation Extraction

History for Visual Dialog: Do we really need it?

2 code implementations ACL 2020 Shubham Agarwal, Trung Bui, Joon-Young Lee, Ioannis Konstas, Verena Rieser

Visual Dialog involves "understanding" the dialog history (what has been discussed previously) and the current question (what is asked), in addition to grounding information in the image, to generate the correct response.

Visual Dialog

Findings of the Third Workshop on Neural Generation and Translation

no code implementations WS 2019 Hiroaki Hayashi, Yusuke Oda, Alexandra Birch, Ioannis Konstas, Andrew Finch, Minh-Thang Luong, Graham Neubig, Katsuhito Sudoh

This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019).

Document-level Machine Translation +1

Automatic Quality Estimation for Natural Language Generation: Ranting (Jointly Rating and Ranking)

1 code implementation WS 2019 Ondřej Dušek, Karin Sevegnani, Ioannis Konstas, Verena Rieser

We present a recurrent neural network based system for automatic quality estimation of natural language generation (NLG) outputs, which jointly learns to assign numerical ratings to individual outputs and to provide pairwise rankings of two different outputs.

Learning-To-Rank Text Generation

Corpus of Multimodal Interaction for Collaborative Planning

no code implementations WS 2019 Miltiadis Marios Katsakioris, Helen Hastie, Ioannis Konstas, Atanas Laskov

As autonomous systems become more commonplace, we need a way to easily and naturally communicate to them our goals and collaboratively come up with a plan on how to achieve these goals.

A Knowledge-Grounded Multimodal Search-Based Conversational Agent

1 code implementation WS 2018 Shubham Agarwal, Ondrej Dusek, Ioannis Konstas, Verena Rieser

Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database.

Question Answering

Improving Context Modelling in Multimodal Dialogue Generation

1 code implementation WS 2018 Shubham Agarwal, Ondrej Dusek, Ioannis Konstas, Verena Rieser

In this work, we investigate the task of textual response generation in a multimodal task-oriented dialogue system.

Dialogue Generation

Better Conversations by Modeling, Filtering, and Optimizing for Coherence and Diversity

1 code implementation EMNLP 2018 Xinnuo Xu, Ond{\v{r}}ej Du{\v{s}}ek, Ioannis Konstas, Verena Rieser

We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity.

Dialogue Generation

Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity

2 code implementations18 Sep 2018 Xinnuo Xu, Ondřej Dušek, Ioannis Konstas, Verena Rieser

We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity.

Mapping Language to Code in Programmatic Context

1 code implementation EMNLP 2018 Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer

To study this phenomenon, we introduce the task of generating class member functions given English documentation and the programmatic context provided by the rest of the class.

Learning a Neural Semantic Parser from User Feedback

no code implementations ACL 2017 Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, Luke Zettlemoyer

We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimal intervention.

SQL Parsing

Story Cloze Task: UW NLP System

no code implementations WS 2017 Roy Schwartz, Maarten Sap, Ioannis Konstas, Leila Zilles, Yejin Choi, Noah A. Smith

This paper describes University of Washington NLP{'}s submission for the Linking Models of Lexical, Sentential and Discourse-level Semantics (LSDSem 2017) shared task{---}the Story Cloze Task.

Language Modelling

A Theme-Rewriting Approach for Generating Algebra Word Problems

no code implementations EMNLP 2016 Rik Koncel-Kedziorski, Ioannis Konstas, Luke Zettlemoyer, Hannaneh Hajishirzi

Texts present coherent stories that have a particular theme or overall setting, for example science fiction or western.

Text Generation

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