# Acl

## Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning (Tsvetkov et al., 2016)

Reordering training sentences for word vectors may impact their usefulness for downstream tasks.

## Neural Semantic Parsing over Multiple Knowledge-bases (Herzig et al., 2017)

Training a single parser on multiple domains can improve performance, and sharing more parameters (encoder and decoder as opposed to just one) seems to help more.

## A Local Detection Approach for Named Entity Recognition and Mention Detection (Xu et al., 2017)

Effective NER can be achieved without sequence prediction using a feedforward network that labels every span with a fixed attention mechanism for getting contextual information.

## Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme (Zheng et al., 2017)

By encoding the relation type and role of each word in tags, an LSTM can be applied to relation extraction with great success.

## Abstractive Document Summarization with a Graph-Based Attentional Neural Model (Tan et al., 2017)

Neural abstractive summarisation can be dramatically improved with a beam search that favours output that matches the source document, and further improved with attention based on PageRank, with a modification to avoid attending to the same sentence more than once.

## Error-repair Dependency Parsing for Ungrammatical Texts (Sakaguchi et al., 2017)

Grammatical error correction can be improved by jointly parsing the sentence being corrected.

## Attention Strategies for Multi-Source Sequence-to-Sequence Learning (Libovicky et al., 2017)

To apply attention across multiple input sources, it is best to apply attention independently and then have a second phase of attention over the summary vectors for each source.

## Robust Incremental Neural Semantic Graph Parsing (Buys et al., 2017)

A neural transition based parser with actions to create non-local links can perform well on Minimal Recursion Semantics parsing.

## A Two-Stage Parsing Method for Text-Level Discourse Analysis (Wang et al., 2017)

Breaking discourse parsing into separate relation identification and labeling tasks can boost performance (by dealing with limited training data).

## A Transition-Based Directed Acyclic Graph Parser for UCCA (Hershcovich et al., 2017)

Parsing performance on the semantic structures of UCCA can be boosted by using a transition system that combines ideas from discontinuous and constituent transition systems, covering the full space of structures.