In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant.
Human language understanding operates at multiple levels of granularity (e. g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined.
Based on this dataset, we propose a Multi-Perspective Context Matching (MPCM) model, which is an end-to-end system that directly predicts the answer beginning and ending points in a passage.
Ranked #3 on Open-Domain Question Answering on SQuAD1.1
Attention-based Neural Machine Translation (NMT) models suffer from attention deficiency issues as has been observed in recent research.
In the training stage, our method induces several sense centroids (embedding) for each polysemous word.
Ranked #4 on Word Sense Induction on SemEval 2010 WSI
Our method simply takes into account the translation options of each word or phrase in the source sentence, and picks a very small target vocabulary for each sentence based on a word-to-word translation model or a bilingual phrase library learned from a traditional machine translation model.
In this paper, we enhance the attention-based neural machine translation (NMT) by adding explicit coverage embedding models to alleviate issues of repeating and dropping translations in NMT.
Most conventional sentence similarity methods only focus on similar parts of two input sentences, and simply ignore the dissimilar parts, which usually give us some clues and semantic meanings about the sentences.
Ranked #7 on Question Answering on WikiQA
In this work, we propose a semi-supervised method for short text clustering, where we represent texts as distributed vectors with neural networks, and use a small amount of labeled data to specify our intention for clustering.