On the WMT 2014 English-to-German and English-to-French translation tasks, this approach yields improvements of 1. 3 BLEU and 0. 3 BLEU over absolute position representations, respectively.
#6 best model for Machine Translation on WMT2014 English-French
We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).
#3 best model for Sentiment Analysis on SST-5 Fine-grained classification
There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam.
#6 best model for Machine Translation on IWSLT2015 German-English
The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i. e. semantic representations) of word sequences as well.
The positive effect of adding subword information to word embeddings has been demonstrated for predictive models.
We propose an unsupervised keyphrase extraction model that encodes topical information within a multipartite graph structure.
In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline.
Sentence pair modeling is critical for many NLP tasks, such as paraphrase identification, semantic textual similarity, and natural language inference.
In this paper we conceptualize extractive summarization as a sentence ranking task and propose a novel training algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.