Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp.net, and will grow over time as additional resources are recast and added from novel sources.Comment: To be presented at EMNLP 2018. 15 page

Adjusting Sense Representations for Word Sense Disambiguation and Automatic Pun Interpretation

Word sense disambiguation (WSD)—the task of determining which meaning a word carries in a particular context—is a core research problem in computational linguistics. Though it has long been recognized that supervised (machine learning–based) approaches to WSD can yield impressive results, they require an amount of manually annotated training data that is often too expensive or impractical to obtain. This is a particular problem for under-resourced languages and domains, and is also a hurdle in well-resourced languages when processing the sort of lexical-semantic anomalies employed for deliberate effect in humour and wordplay. In contrast to supervised systems are knowledge-based techniques, which rely only on pre-existing lexical-semantic resources (LSRs). These techniques are of more general applicability but tend to suffer from lower performance due to the informational gap between the target word's context and the sense descriptions provided by the LSR. This dissertation is concerned with extending the efficacy and applicability of knowledge-based word sense disambiguation. First, we investigate two approaches for bridging the information gap and thereby improving the performance of knowledge-based WSD. In the first approach we supplement the word's context and the LSR's sense descriptions with entries from a distributional thesaurus. The second approach enriches an LSR's sense information by aligning it to other, complementary LSRs. Our next main contribution is to adapt techniques from word sense disambiguation to a novel task: the interpretation of puns. Traditional NLP applications, including WSD, usually treat the source text as carrying a single meaning, and therefore cannot cope with the intentionally ambiguous constructions found in humour and wordplay. We describe how algorithms and evaluation methodologies from traditional word sense disambiguation can be adapted for the "disambiguation" of puns, or rather for the identification of their double meanings. Finally, we cover the design and construction of technological and linguistic resources aimed at supporting the research and application of word sense disambiguation. Development and comparison of WSD systems has long been hampered by a lack of standardized data formats, language resources, software components, and workflows. To address this issue, we designed and implemented a modular, extensible framework for WSD. It implements, encapsulates, and aggregates reusable, interoperable components using UIMA, an industry-standard information processing architecture. We have also produced two large sense-annotated data sets for under-resourced languages or domains: one of these targets German-language text, and the other English-language puns

Duluth at SemEval-2017 Task 6: Language Models in Humor Detection

This paper describes the Duluth system that participated in SemEval-2017 Task 6 #HashtagWars: Learning a Sense of Humor. The system participated in Subtasks A and B using N-gram language models, ranking highly in the task evaluation. This paper discusses the results of our system in the development and evaluation stages and from two post-evaluation runs.Comment: 5 pages, to Appear in the Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval 2017), August 2017, Vancouver, B