ACL 2017

Reading Wikipedia to Answer Open-Domain Questions

ACL 2017 facebookresearch/ParlAI

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article.

OPEN-DOMAIN QUESTION ANSWERING READING COMPREHENSION

A Convolutional Encoder Model for Neural Machine Translation

ACL 2017 facebookresearch/fairseq

The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence.

MACHINE TRANSLATION

Hybrid Code Networks: practical and efficient end-to-end dialog control with supervised and reinforcement learning

ACL 2017 deepmipt/DeepPavlov

End-to-end learning of recurrent neural networks (RNNs) is an attractive solution for dialog systems; however, current techniques are data-intensive and require thousands of dialogs to learn simple behaviors.

OpenNMT: Open-Source Toolkit for Neural Machine Translation

ACL 2017 OpenNMT/OpenNMT

We describe an open-source toolkit for neural machine translation (NMT).

MACHINE TRANSLATION

Get To The Point: Summarization with Pointer-Generator Networks

ACL 2017 abisee/pointer-generator

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).

ABSTRACTIVE TEXT SUMMARIZATION

Deep Keyphrase Generation

ACL 2017 memray/seq2seq-keyphrase

Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content.

Deep Semantic Role Labeling: What Works and What's Next

ACL 2017 luheng/deep_srl

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations.

PREDICATE DETECTION

Multimodal Word Distributions

ACL 2017 benathi/word2gm

Word embeddings provide point representations of words containing useful semantic information.

WORD EMBEDDINGS

Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

ACL 2017 crazydonkey200/neural-symbolic-machines

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base.

FEATURE ENGINEERING STRUCTURED PREDICTION