Guillaume Bouchard


Interpretation of Natural Language Rules in Conversational Machine Reading
Marzieh Saeidi | Max Bartolo | Patrick Lewis | Sameer Singh | Tim Rocktäschel | Mike Sheldon | Guillaume Bouchard | Sebastian Riedel
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. However, many real-world question answering problems require the reading of text not because it contains the literal answer, but because it contains a recipe to derive an answer together with the reader’s background knowledge. One example is the task of interpreting regulations to answer “Can I...?” or “Do I have to...?” questions such as “I am working in Canada. Do I have to carry on paying UK National Insurance?” after reading a UK government website about this topic. This task requires both the interpretation of rules and the application of background knowledge. It is further complicated due to the fact that, in practice, most questions are underspecified, and a human assistant will regularly have to ask clarification questions such as “How long have you been working abroad?” when the answer cannot be directly derived from the question and text. In this paper, we formalise this task and develop a crowd-sourcing strategy to collect 37k task instances based on real-world rules and crowd-generated questions and scenarios. We analyse the challenges of this task and assess its difficulty by evaluating the performance of rule-based and machine-learning baselines. We observe promising results when no background knowledge is necessary, and substantial room for improvement whenever background knowledge is needed.


Learning to Generate Textual Data
Guillaume Bouchard | Pontus Stenetorp | Sebastian Riedel
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

A Factorization Machine Framework for Testing Bigram Embeddings in Knowledgebase Completion
Johannes Welbl | Guillaume Bouchard | Sebastian Riedel
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

Defining Words with Words: Beyond the Distributional Hypothesis
Iuliana-Elena Parasca | Andreas Lukas Rauter | Jack Roper | Aleksandar Rusinov | Guillaume Bouchard | Sebastian Riedel | Pontus Stenetorp
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

Orthogonality regularizer for question answering
Chunyang Xiao | Guillaume Bouchard | Marc Dymetman | Claire Gardent
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods
Marzieh Saeidi | Guillaume Bouchard | Maria Liakata | Sebastian Riedel
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis – that assumes a single entity per document — and targeted sentiment analysis — that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighbourhoods are discussed by users. In this context units of text often mention several aspects of one or more neighbourhoods. This is the first time that a generic social media platform,i.e. QA, is used for fine-grained opinion mining. Text coming from QA platforms are far less constrained compared to text from review specific platforms which current datasets are based on. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks


Matrix and Tensor Factorization Methods for Natural Language Processing
Guillaume Bouchard | Jason Naradowsky | Sebastian Riedel | Tim Rocktäschel | Andreas Vlachos
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing: Tutorial Abstracts


Structured Penalties for Log-Linear Language Models
Anil Kumar Nelakanti | Cédric Archambeau | Julien Mairal | Francis Bach | Guillaume Bouchard
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing


Optimization and Sampling for NLP from a Unified Viewpoint
Marc Dymetman | Guillaume Bouchard | Simon Carter
Proceedings of the First International Workshop on Optimization Techniques for Human Language Technology

Exact Sampling and Decoding in High-Order Hidden Markov Models
Simon Carter | Marc Dymetman | Guillaume Bouchard
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning


Named Entity Generation Using Sampling-based Structured Prediction
Guillaume Bouchard
Proceedings of the 6th International Natural Language Generation Conference