Gábor Melis


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Unsupervised Recurrent Neural Network Grammars
Yoon Kim | Alexander Rush | Lei Yu | Adhiguna Kuncoro | Chris Dyer | Gábor Melis
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. Supervised RNNGs achieve strong language modeling and parsing performance, but require an annotated corpus of parse trees. In this work, we experiment with unsupervised learning of RNNGs. Since directly marginalizing over the space of latent trees is intractable, we instead apply amortized variational inference. To maximize the evidence lower bound, we develop an inference network parameterized as a neural CRF constituency parser. On language modeling, unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese. On constituency grammar induction, they are competitive with recent neural language models that induce tree structures from words through attention mechanisms.


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The NarrativeQA Reading Comprehension Challenge
Tomáš Kočiský | Jonathan Schwarz | Phil Blunsom | Chris Dyer | Karl Moritz Hermann | Gábor Melis | Edward Grefenstette
Transactions of the Association for Computational Linguistics, Volume 6

Reading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existing RC datasets and tasks are dominated by questions that can be solved by selecting answers using superficial information (e.g., local context similarity or global term frequency); they thus fail to test for the essential integrative aspect of RC. To encourage progress on deeper comprehension of language, we present a new dataset and set of tasks in which the reader must answer questions about stories by reading entire books or movie scripts. These tasks are designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard RC models struggle on the tasks presented here. We provide an analysis of the dataset and the challenges it presents.


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Semantic Parsing with Semi-Supervised Sequential Autoencoders
Tomáš Kočiský | Gábor Melis | Edward Grefenstette | Chris Dyer | Wang Ling | Phil Blunsom | Karl Moritz Hermann
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing