Research Area
Year Published

66 Results

October 31, 2018

Neural Compositional Denotational Semantics for Question Answering

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Answering compositional questions requiring multi-step reasoning is challenging. We introduce an end-to-end differentiable model for interpreting questions about a knowledge graph (KG), which is inspired by formal approaches to semantics.

By: Nitish Gupta, Mike Lewis
October 31, 2018

Auto-Encoding Dictionary Definitions into Consistent Word Embeddings

Empirical Methods in Natural Language Processing (EMNLP)

Monolingual dictionaries are widespread and semantically rich resources. This paper presents a simple model that learns to compute word embeddings by processing dictionary definitions and trying to reconstruct them.

By: Tom Bosc, Pascal Vincent
October 31, 2018

Semantic Parsing for Task Oriented Dialog using Hierarchical Representations

Conference on Empirical Methods in Natural Language Processing (EMNLP)

Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on…

By: Sonal Gupta, Rushin Shah, Mrinal Mohit, Anuj Kumar, Mike Lewis
October 31, 2018

A Dataset for Telling the Stories of Social Media Videos

Empirical Methods in Natural Language Processing (EMNLP)

Video content on social media platforms constitutes a major part of the communication between people, as it allows everyone to share their stories. However, if someone is unable to consume video, either due to a disability or network bandwidth, this severely limits their participation and communication.

By: Spandana Gella, Mike Lewis, Marcus Rohrbach
October 31, 2018

Understanding Back-Translation at Scale

Empirical Methods in Natural Language Processing (EMNLP)

An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. This work broadens the understanding of back-translation and investigates a number of methods to generate synthetic source sentences.

By: Sergey Edunov, Myle Ott, Michael Auli, David Grangier
October 31, 2018

Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Workshop on Search-Oriented Conversational AI (SCAI) at EMNLP

In this work we develop a model that combines the two approaches to avoid both their deficiencies: first retrieve a response and then refine it – the final sequence generator treating the retrieval as additional context.

By: Jason Weston, Emily Dinan, Alexander H. Miller
October 31, 2018

Phrase-Based & Neural Unsupervised Machine Translation

Empirical Methods in Natural Language Processing (EMNLP)

Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of bitexts, which hinders their applicability to the majority of language pairs. This work investigates how to learn to translate when having access to only large monolingual corpora in each language. We propose two model variants, a neural and a phrase-based model.

By: Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
October 30, 2018

Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

Empirical Methods in Natural Language Processing (EMNLP)

Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space. Existing works typically solve a least-square regression problem to learn a rotation aligning a small bilingual lexicon, and use a retrieval criterion for inference. In this paper, we propose an unified formulation that directly optimizes a retrieval criterion in an end-to-end fashion.

By: Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Hervé Jégou, Edouard Grave
October 30, 2018

Scaling Neural Machine Translation

Conference on Machine Translation (WMT)

Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that reduced precision and large batch training can speedup training by nearly 5x on a single 8- GPU machine with careful tuning and implementation.

By: Myle Ott, Sergey Edunov, David Grangier, Michael Auli
October 29, 2018

XNLI: Evaluating Cross-lingual Sentence Representations

Empirical Methods in Natural Language Processing (EMNLP)

In this work, we construct an evaluation set for XLU by extending the development and test sets of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15 languages, including low-resource languages such as Swahili and Urdu.

By: Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel R. Bowman, Holger Schwenk, Ves Stoyanov