ACL 2017

Semi-supervised sequence tagging with bidirectional language models

ACL 2017 zalandoresearch/flair

Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.

CHUNKING NAMED ENTITY RECOGNITION WORD EMBEDDINGS

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

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.

Get To The Point: Summarization with Pointer-Generator Networks

ACL 2017 atulkum/pointer_summarizer

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

Semi-supervised Multitask Learning for Sequence Labeling

ACL 2017 marekrei/sequence-labeler

We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset.

CHUNKING GRAMMATICAL ERROR DETECTION LANGUAGE MODELLING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING

Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders

ACL 2017 snakeztc/NeuralDialog-CVAE

While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses.

DECISION MAKING DIALOGUE GENERATION

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.

STRUCTURED PREDICTION