基于注意機(jī)制編碼解碼模型的答案選擇方法(英文)
本文選題:問(wèn)答技術(shù) + 答案選擇; 參考:《Frontiers of Information Technology & Electronic Engineering》2017年04期
【摘要】:問(wèn)答技術(shù)的重要挑戰(zhàn)之一就是解決問(wèn)題與答案之間的語(yǔ)義空白。機(jī)器翻譯模型已經(jīng)被證明能有效的提升解決問(wèn)題與答案之間的語(yǔ)義空白。本文提出了一種基于注意機(jī)制的深度神經(jīng)網(wǎng)絡(luò)模型來(lái)解決問(wèn)答系統(tǒng)中的答案選擇任務(wù)。該模型采用了基于雙向長(zhǎng)短時(shí)記憶(Long short-term memory,LSTM)的編碼解碼模型,編碼解碼模型是一個(gè)被證明再機(jī)器翻譯領(lǐng)域取得了突出的成績(jī)。我們還在模型中應(yīng)用了注意力機(jī)制來(lái)提升模型的效果。本文在一個(gè)公開數(shù)據(jù)集上驗(yàn)證了實(shí)驗(yàn)的有效性,同時(shí)通過(guò)結(jié)合該模型顯著提高了問(wèn)答系統(tǒng)的性能在TREC 2015 liveQA的任務(wù)中。
[Abstract]:One of the important challenges of Q & A is to solve the semantic gap between questions and answers. Machine translation models have been shown to effectively improve the semantic gap between problem solving and answers. In this paper, a depth neural network model based on attention mechanism is proposed to solve the problem of answer selection in question answering system. The model is based on bidirectional long short-term memory (LSTM) coding and decoding model, which is proved to have achieved outstanding results in the field of machine translation. We also applied the attention mechanism in the model to improve the effectiveness of the model. This paper verifies the effectiveness of the experiment on an open data set and improves the performance of the Q & A system significantly by combining the model with the task of TREC 2015 liveQA.
【作者單位】: College
【基金】:Project supported by the National Basic Research Program(973)of China(Nos.2013CB329601 and 2013CB329604) the National Natural Science Foundation of China(Nos.61372191,61202362,and 61472433)
【分類號(hào)】:TP391.2;TP183
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