Selecting answers with structured lexical expansion and discourse relations: LIMSI's participation at QA4MRE 2013

International audiencen this paper, we present the LIMSI’s participation to QA4MRE2013. We decided to test two kinds of methods. The first one focuses on complex questions, such as causal questions, and exploits discourse relations. Relation recognition shows promising results, however it has to be improved to have an impact on answer selection. The second method is based on semantic variations. We explored the English Wiktionary to find reformulations of words in the definitions, and used these reformulations to index the documents and select passages in the Entrance exams task

A hierarchical taxonomy for classifying hardness of inference tasks

International audienceExhibiting inferential capabilities is one of the major goals of many modern Natural Language Processing systems. However, if attempts have been made to define what textual inferences are, few seek to classify inference phenomena by difficulty. In this paper we propose a hierarchical taxonomy for inferences, relatively to their hardness, and with corpus annotation and system design and evaluation in mind. Indeed, a fine-grained assessment of the difficulty of a task allows us to design more appropriate systems and to evaluate them only on what they are designed to handle. Each of seven classes is described and provided with examples from different tasks like question answering, textual entailment and coreference resolution. We then test the classes of our hierarchy on the specific task of question answering. Our annotation process of the testing data at the QA4MRE 2013 evaluation campaign reveals that it is possible to quantify the contrasts in types of difficulty on datasets of the same task

English machine reading comprehension: new approaches to answering multiple-choice questions

Reading comprehension is often tested by measuring a person or system’s ability to answer questions about a given text. Machine reading comprehension datasets have proliferated in recent years, particularly for the English language. The aim of this thesis is to investigate and improve data-driven approaches to automatic reading comprehension. Firstly, I provide a full classification of question and answer types for the reading comprehension task. I also present a systematic overview of English reading comprehension datasets (over 50 datasets). I observe that the majority of questions were created using crowdsourcing and the most popular data source is Wikipedia. There is also a lack of why, when, and where questions. Additionally, I address the question “What makes a dataset difficult?” and highlight the difference between datasets created for people and datasets created for machine reading comprehension. Secondly, focusing on multiple-choice question answering, I propose a computationally light method for answer selection based on string similarities and logistic regression. At the time (December 2017), the proposed approach showed the best performance on two datasets (MovieQA and MCQA: IJCNLP 2017 Shared Task 5 Multi-choice Question Answering in Examinations) outperforming some CNN-based methods. Thirdly, I investigate methods for Boolean Reading Comprehension tasks including the use of Knowledge Graph (KG) information for answering questions. I provide an error analysis of a transformer model’s performance on the BoolQ dataset. This reveals several important issues such as unstable model behaviour and some issues with the dataset itself. Experiments with incorporating knowledge graph information into a baseline transformer model do not show a clear improvement due to a combination of the model’s ability to capture new information, inaccuracies in the knowledge graph, and imprecision in entity linking. Finally, I develop a Boolean Reading Comprehension dataset based on spontaneously user-generated questions and reviews which is extremely close to a real-life question-answering scenario. I provide a classification of question difficulty and establish a transformer-based baseline for the new proposed dataset

REVISITING RECOGNIZING TEXTUAL ENTAILMENT FOR EVALUATING NATURAL LANGUAGE PROCESSING SYSTEMS

Recognizing Textual Entailment (RTE) began as a unified framework to evaluate the reasoning capabilities of Natural Language Processing (NLP) models. In recent years, RTE has evolved in the NLP community into a task that researchers focus on developing models for. This thesis revisits the tradition of RTE as an evaluation framework for NLP models, especially in the era of deep learning. Chapter 2 provides an overview of different approaches to evaluating NLP sys- tems, discusses prior RTE datasets, and argues why many of them do not serve as satisfactory tests to evaluate the reasoning capabilities of NLP systems. Chapter 3 presents a new large-scale diverse collection of RTE datasets (DNC) that tests how well NLP systems capture a range of semantic phenomena that are integral to un- derstanding human language. Chapter 4 demonstrates how the DNC can be used to evaluate reasoning capabilities of NLP models. Chapter 5 discusses the limits of RTE as an evaluation framework by illuminating how existing datasets contain biases that may enable crude modeling approaches to perform surprisingly well. The remaining aspects of the thesis focus on issues raised in Chapter 5. Chapter 6 addresses issues in prior RTE datasets focused on paraphrasing and presents a high-quality test set that can be used to analyze how robust RTE systems are to paraphrases. Chapter 7 demonstrates how modeling approaches on biases, e.g. adversarial learning, can enable RTE models overcome biases discussed in Chapter 5. Chapter 8 applies these methods to the task of discovering emergency needs during disaster events