本文選題：答案選擇　切入點(diǎn)：深度學(xué)習(xí)　出處：《哈爾濱工業(yè)大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：答案選擇是給定一個問題和該問題的候選答案列表,根據(jù)問題和答案的相關(guān)度對候選列表中的答案進(jìn)行重新排序。在答案選擇任務(wù)中,大多數(shù)問題和答案之間單詞的重合率和相似度并不高,且語義也不相似,很難使用單詞或文本相似來解決,這給傳統(tǒng)的特征工程方法帶來了一些困難。近年來,深度學(xué)習(xí)已經(jīng)在很多自然語言處理領(lǐng)域取得了不錯的成績,如文本蘊(yùn)含、機(jī)器翻譯和問答系統(tǒng)等。利用深度學(xué)習(xí)技術(shù)處理答案選擇任務(wù),不是單純的提取單詞或單詞組合特征,而是從語義層面上對句子進(jìn)行理解,得到問題和答案的相關(guān)度信息。在本文中,將句子單詞向量化后,利用深度學(xué)習(xí)技術(shù)建立句子編碼模型獲取問題和答案的句子向量,后使用相關(guān)度計(jì)算方法計(jì)算問題和答案相關(guān)度,對候選答案進(jìn)行排序。本文的主要研究方向從以下三個方向進(jìn)行:⑴基于卷積神經(jīng)網(wǎng)絡(luò)的答案選擇模型。實(shí)現(xiàn)了基于卷積神經(jīng)網(wǎng)絡(luò)的答案選擇模型,使用卷積神經(jīng)網(wǎng)絡(luò)對問題和答案進(jìn)行編碼,提取句子中的語義特征,最終通過相關(guān)性矩陣計(jì)算得到問題向量和答案向量的相關(guān)度。⑵基于長短期記憶網(wǎng)絡(luò)和注意力機(jī)制的答案選擇模型。循環(huán)神經(jīng)網(wǎng)絡(luò)擅長處理序列信息,可以存儲歷史信息,并且可以捕捉到單詞位置不同帶來的語義變化。本文實(shí)現(xiàn)了一種基于長短期記憶網(wǎng)絡(luò)和注意力機(jī)制的句子編碼模型,并提出了一種自動學(xué)習(xí)非文本特征的方法,與基于Attention-LSTM的答案選擇模型相結(jié)合后,比Attention-LSTM模型效果有所提高。⑶基于雙向長短期記憶網(wǎng)絡(luò)和自動編碼器的答案選擇模型。Bi LSTM模型相對于LSTM模型,在對句子編碼時同時考慮上下文信息,最終得到的句子編碼更加完整。本文實(shí)現(xiàn)了基于Bi LSTM的答案選擇模型,后期為了使句子編碼模型參數(shù)訓(xùn)練的更加充分,實(shí)現(xiàn)了一種基于Seq2Seq的自動編碼模型,對基于Bi LSTM的句子編碼模型參數(shù)進(jìn)行預(yù)訓(xùn)練。實(shí)驗(yàn)結(jié)果表明,經(jīng)過預(yù)訓(xùn)練的基于Bi LSTM的答案選擇模型性能更優(yōu),與非文本特征結(jié)合后,整體模型在測試集上的結(jié)果已經(jīng)高于了基線系統(tǒng)的最優(yōu)結(jié)果。
[Abstract]:The answer selection is to reorder the answers in the candidate list based on the relevance of the question and the answer given a question and a list of candidates for that question. The coincidence rate and similarity between most questions and answers are not high, and the semantics are not similar, so it is difficult to solve the problem by using the word or text similarity, which brings some difficulties to the traditional feature engineering methods in recent years. Deep learning has achieved good results in many fields of natural language processing, such as text implication, machine translation and question answering system. In this paper, after the sentence words are vectorized, the sentence encoding model is established to obtain the sentence vector of the question and the answer. Then using the correlation calculation method to calculate the correlation between the question and the answer, The main research direction of this paper is to carry out the answer selection model based on convolution neural network in the following three directions: 1. The answer selection model based on convolutional neural network is realized. Using convolutional neural network to encode the questions and answers, the semantic features of sentences are extracted. Finally, the correlation between the question vector and the answer vector is obtained by calculating the correlation matrix. 2. The model of choice of answer is based on the long-term and short-term memory network and attention mechanism. The cyclic neural network is good at processing sequence information and can store historical information. In this paper, a sentence coding model based on short and long term memory network and attention mechanism is implemented, and an automatic learning method of non-text features is proposed. Combined with the answer selection model based on Attention-LSTM, the effect of Attention-LSTM model is better than that of Attention-LSTM model. 3. The answer selection model. Bi LSTM model based on bidirectional long and short memory network and automatic encoder is relative to LSTM model. In this paper, the answer selection model based on Bi LSTM is implemented, in order to train the parameters of the sentence coding model more fully. An automatic coding model based on Seq2Seq is implemented, and the parameters of sentence coding model based on Bi LSTM are pre-trained. The experimental results show that the pre-trained answer selection model based on Bi LSTM has better performance and is combined with non-text features. The results of the whole model on the test set are higher than the optimal results of the baseline system.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18

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