Distributional models of semantics have proven themselves invaluable both in cognitive modelling of semantic phenomena and also in practical applications. For example, they have been used to model judgments of semantic similarity (McDonald, 2000) and association (Denhire and Lemaire, 2004;Griffiths et al., 2007) and have been shown to achieve human level performance on synonymy tests (Landuaer and Dumais, 1997;Griffiths et al., 2007) such as those included in the Test of English as Foreign Language (TOEFL). This ability has been put to practical use in automatic thesaurus extraction (Grefenstette, 1994). However, while there has been a considerable amount of research directed at the most effective ways of constructing representations for individual words, the representation of larger constructions, e.g., phrases and sentences, has received relatively little attention. In this thesis we examine this issue of how to compose meanings within distributional models of semantics to form representations of multi-word structures.Natural language data typically consists of such complex structures, rather than just individual isolated words. Thus, a model of composition, in which individual word meanings are combined into phrases and phrases combine to form sentences, is of central importance in modelling this data. Commonly, however, distributional representations are combined in terms of addition (Landuaer and Dumais, 1997;Foltz et al., 1998), without any empirical evaluation of alternative choices. Constructing effective distributional representations of phrases and sentences requires that we have both a theoretical foundation to direct the development of models of composition and also a means of empirically evaluating those models.The approach we take is to first consider the general properties of semantic composition and from that basis define a comprehensive framework in which to consider the composition of distributional representations. The framework subsumes existing proposals, such as addition and tensor products, but also allows us to define novel composition functions. We then show that the effectiveness of these models can be i evaluated on three empirical tasks.The first of these tasks involves modelling similarity judgements for short phrases gathered in human experiments. Distributional representations of individual words are commonly evaluated on tasks based on their ability to model semantic similarity relations, e.g., synonymy or priming. Thus, it seems appropriate to evaluate phrase representations in a similar manner. We then apply compositional models to language modelling, demonstrating that the issue of composition has practical consequences, and also providing an evaluation based on large amounts of natural data. In our third task, we use these language models in an analysis of reading times from an eye-movement study. This allows us to investigate the relationship between the composition of distributional representations and the processes involved in comprehending phrases and sentences.We ...

In this paper we propose a novel statistical language model to capture long-range semantic dependencies. Specifically, we apply the concept of semantic composition to the problem of constructing predictive history representations for upcoming words. We also examine the influence of the underlying semantic space on the composition task by comparing spatial semantic representations against topic-based ones. The composition models yield reductions in perplexity when combined with a standard n-gram language model over the n-gram model alone. We also obtain perplexity reductions when integrating our models with a structured language model.

In this paper we describe our 2 nd place FEVER shared-task system that achieved a FEVER score of 62.52% on the provisional test set (without additional human evaluation), and 65.41% on the development set. Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact. The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.

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Natural Language Inference is a challenging task that has received substantial attention, and state-of-the-art models now achieve impressive test set performance in the form of accuracy scores. Here, we go beyond this single evaluation metric to examine robustness to semantically-valid alterations to the input data. We identify three factors -insensitivity, polarity and unseen pairs -and compare their impact on three SNLI models under a variety of conditions. Our results demonstrate a number of strengths and weaknesses in the models' ability to generalise to new in-domain instances. In particular, while strong performance is possible on unseen hypernyms, unseen antonyms are more challenging for all the models. More generally, the models suffer from an insensitivity to certain small but semantically significant alterations, and are also often influenced by simple statistical correlations between words and training labels. Overall, we show that evaluations of NLI models can benefit from studying the influence of factors intrinsic to the models or found in the dataset used.