Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method. We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment approach to soft-align questions and answers with justification sentences using only GloVe embeddings, (b) an iterative process that reformulates queries focusing on terms that are not covered by existing justifications, which (c) a stopping criterion that terminates retrieval when the terms in the given question and candidate answers are covered by the retrieved justifications. Despite its simplicity, our approach outperforms all the previous methods (including supervised methods) on the evidence selection task on two datasets: MultiRC and QASC. When these evidence sentences are fed into a RoBERTa answer classification component, we achieve state-of-the-art QA performance on these two datasets.Comment: Accepted at ACL 2020 as a long conference pape

Generating natural language specifications from UML class diagrams

Early phases of software development are known to be problematic, difficult to manage and errors occurring during these phases are expensive to correct. Many systems have been developed to aid the transition from informal Natural Language requirements to semistructured or formal specifications. Furthermore, consistency checking is seen by many software engineers as the solution to reduce the number of errors occurring during the software development life cycle and allow early verification and validation of software systems. However, this is confined to the models developed during analysis and design and fails to include the early Natural Language requirements. This excludes proper user involvement and creates a gap between the original requirements and the updated and modified models and implementations of the system. To improve this process, we propose a system that generates Natural Language specifications from UML class diagrams. We first investigate the variation of the input language used in naming the components of a class diagram based on the study of a large number of examples from the literature and then develop rules for removing ambiguities in the subset of Natural Language used within UML. We use WordNet,a linguistic ontology, to disambiguate the lexical structures of the UML string names and generate semantically sound sentences. Our system is developed in Java and is tested on an independent though academic case study

Individual and Domain Adaptation in Sentence Planning for Dialogue

One of the biggest challenges in the development and deployment of spoken dialogue systems is the design of the spoken language generation module. This challenge arises from the need for the generator to adapt to many features of the dialogue domain, user population, and dialogue context. A promising approach is trainable generation, which uses general-purpose linguistic knowledge that is automatically adapted to the features of interest, such as the application domain, individual user, or user group. In this paper we present and evaluate a trainable sentence planner for providing restaurant information in the MATCH dialogue system. We show that trainable sentence planning can produce complex information presentations whose quality is comparable to the output of a template-based generator tuned to this domain. We also show that our method easily supports adapting the sentence planner to individuals, and that the individualized sentence planners generally perform better than models trained and tested on a population of individuals. Previous work has documented and utilized individual preferences for content selection, but to our knowledge, these results provide the first demonstration of individual preferences for sentence planning operations, affecting the content order, discourse structure and sentence structure of system responses. Finally, we evaluate the contribution of different feature sets, and show that, in our application, n-gram features often do as well as features based on higher-level linguistic representations

Three Approaches to Generating Texts in Different Styles

Natural Language Generation (nlg) systems generate texts in English and other human languages from non-linguistic input data. Usually there are a large number of possible texts that can communicate the input data, and nlg systems must choose one of these. We argue that style can be used by nlg systems to choose between possible texts, and explore how this can be done by (1) explicit stylistic parameters, (2) imitating a genre style, and (3) imitating an individual’s style

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies