Research Area
Year Published

844 Results

July 27, 2019

The Referential Reader: A Recurrent Entity Network for Anaphora Resolution

Association for Computational Linguistics (ACL)

We present a new architecture for storing and accessing entity mentions during online text processing. While reading the text, entity references are identified, and may be stored by either updating or overwriting a cell in a fixed-length memory.

By: Fei Liu, Luke Zettlemoyer, Jacob Eisenstein

July 27, 2019

Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings

Association for Computational Linguistics (ACL)

Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings.

By: Mikel Artetxe, Holger Schwenk

July 27, 2019

Adaptive Attention Span in Transformers

Association for Computational Linguistics (ACL)

We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational time.

By: Sainbayar Sukhbaatar, Edouard Grave, Piotr Bojanowski, Armand Joulin

July 26, 2019

Strategies for Structuring Story Generation

Association for Computational Linguistics (ACL)

Writers often rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities.

By: Angela Fan, Mike Lewis, Yann Dauphin

July 26, 2019

On the Distribution of Deep Clausal Embeddings: A Large Cross-linguistic Study

Association for Computational Linguistics (ACL)

We introduce here a collection of large, dependency-parsed written corpora in 17 languages, that allow us, for the first time, to capture clausal embedding through dependency graphs and assess their distribution.

By: Damián E. Blasi, Ryan Cotterell, Lawrence Wolf-Sonkin, Sabine Stoll, Balthasar Bickel, Marco Baroni

July 18, 2019

Tabula nearly rasa: Probing the linguistic knowledge of character-level neural language models trained on unsegmented text

Topology, Algebra and Categories in Logic (TACL)

Recurrent neural networks (RNNs) have reached striking performance in many natural language processing tasks. This has renewed interest in whether these generic sequence processing devices are inducing genuine linguistic knowledge. Nearly all current analytical studies, however, initialize the RNNs with a vocabulary of known words, and feed them tokenized input during training. We present a multi-lingual study of the linguistic knowledge encoded in RNNs trained as character-level language models, on input data with word boundaries removed.

By: Michael Hahn, Marco Baroni

July 18, 2019

Why Build an Assistant in Minecraft?

arXiv

In this document we describe a rationale for a research program aimed at building an open “assistant” in the game
Minecraft, in order to make progress on the problems of natural language understanding and learning from dialogue.

By: Arthur Szlam, Jonathan Gray, Kavya Srinet, Yacine Jernite, Armand Joulin, Gabriel Synnaeve, Douwe Kiela, Haonan Yu, Zhuoyuan Chen, Siddharth Goyal, Demi Guo, Danielle Rothermel, Larry Zitnick, Jason Weston

July 17, 2019

CraftAssist: A Framework for Dialogue-enabled Interactive Agents

This paper describes an implementation of a bot assistant in Minecraft, and the tools and platform allowing players to interact with the bot and to record those interactions. The purpose of building such an assistant is to facilitate the study of agents that can complete tasks specified by dialogue, and eventually, to learn from dialogue interactions.

By: Jonathan Gray, Kavya Srinet, Yacine Jernite, Haonan Yu, Zhuoyuan Chen, Demi Guo, Siddharth Goyal, Larry Zitnick, Arthur Szlam

July 15, 2019

Searching for Communities: a Facebook Way

ACM SIGIR Conference on Research and Development in Information Retrieval

Giving people the power to build community is central to Facebook’s mission. Technically, searching for communities poses very different challenges compared to the standard IR problems.

By: Viet Ha-Thuc, Srinath Aaleti, Rongda Zhu, Nade Sritanyaratana, Corey Chen

July 14, 2019

Extreme Relative Pose Estimation for RGB-D Scans via Scene Completion

Conference on Computer Vision and Pattern Recognition (CVPR)

Estimating the relative rigid pose between two RGB-D scans of the same underlying environment is a fundamental problem in computer vision, robotics, and computer graphics. Most existing approaches allow only limited relative pose changes since they require considerable overlap between the input scans. We introduce a novel approach that extends the scope to extreme relative poses, with little or even no overlap between the input scans.

By: Zhenpei Yang, Jeffrey Z. Pan, Linjie Luo, Xiaowei Zhou, Kristen Grauman, Qixing Huang