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摘要: 谷歌的人工智能系統（AlphaGo）在圍棋領域取得了一系列成功，使得深度強化學習得到越來越多的關注。深度強化學習融合了深度學習對復雜環境的感知能力和強化學習對復雜情景的決策能力。而自然語言處理過程中有著數量巨大的詞匯或者語句需要表征，并且在對話系統、機器翻譯和圖像描述等文本生成任務中存在大量難以建模的決策問題。這使得深度強化學習在自然語言處理的文本生成任務中能夠發揮重要的作用，幫助改進現有的模型結構或者訓練機制，并且已經取得了很多顯著的成果。為此，本文系統闡述深度強化學習應用在不同的文本生成任務中的一些主要方法，梳理其發展的軌跡，分析算法特點。最后，展望深度強化學習與自然語言處理任務融合的前景和挑戰。Abstract: With the recent exciting achievements of Google’s artificial intelligence system in the game of Go, deep reinforcement learning (DRL) has witnessed considerable development. DRL combines the abilities of sensing and making decisions provided by deep learning and reinforcement learning. Natural language processing (NLP) involves a large number of vocabularies or statements that have to be represented, and its subtasks, such as the dialogue system and machine translation, involve many decision problems that are difficult to model. Because of the aforementioned reasons, DRL can be appropriately applied to various NLP tasks such as named entity recognition, relation extraction, dialogue system, image caption, and machine translation. Further, DRL is helpful in improving the framework or the training pipeline of the aforementioned tasks, and notable achievements have been obtained. DRL is not an algorithm or a method but a paradigm. Many researchers fit plenty of NLP tasks in this paradigm and achieve better performance. Specifically, in text generation based on the reinforcement learning paradigm, the learning process that is used to produce a predicted sequence from the given source sequence can be considered to be the Markov decision process (MDP). In MDP, an agent interacts with the environment by receiving a sequence of observations and scaled rewards and subsequently produces the next action or word. This causes the text generation model to achieve decision-making ability, which can result in future success. Thus, the text generation task integrated with reinforcement learning is an attractive and promising research field. This study presented a comprehensive introduction and a systemic overview. First, we presented the basic methods in DRL and its variations. Then, we showed the main applications of DRL during the text generation task, trace the development of DRL, and summarized the merits and demerits associated with these applications. The final section enumerated some future research directions of DRL combined with NLP.
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表 1 對話數據集內容概覽
Table 1. Summary of dialogue datasets
Dataset Numbers of dialogue Numbers of slots Scene Multi-turn Cambridge restaurants database 720 6 1 Yes San Francisco restaurants database 3577 12 1 Yes Dialog system technology challenge 2 3000 8 1 Yes Dialog system technology challenge 3 2265 9 1 Yes Stanford multi-turn multi-domain task-oriented dialogue dataset 3031 79，65，140 3 Yes The Twitter dialogue corpus 1300000 — — Yes The Ubuntu dialogue corpus 932429 — — No Opensubtitle corpus 70000000 — — No 
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