Abstract: Open domain response generation has achieved remarkable progress in recent years, but sometimes yields short and uninformative responses. We propose a new paradigm for response generation, that is response generation by editing, which significantly increases the diversity and informativeness of the generation results. Our assumption is that a plausible response can be generated by slightly revising an existing response prototype. The prototype is retrieved from a pre-defined index and provides a good start-point for generation because it is grammatical and informative. We design a response editing model, where an edit vector is formed by considering differences between a prototype context and a current context, and then the edit vector is fed to a decoder to revise the prototype response for the current context. Experiment results on a large scale dataset demonstrate that the response editing model outperforms generative and retrieval-based models on various aspects.

Introduction: In recent years, non-task oriented chatbots focused on responding to humans intelligently on a variety of topics, have drawn much attention from both academia and industry. Existing approaches can be categorized into generation-based methods BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 which generate a response from scratch, and retrieval-based methods BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 which select a response from an existing corpus. Since retrieval-based approaches are severely constrained by a pre-defined index, generative approaches become more and more popular in recent years. Traditional generation-based approaches, however, do not easily generate long, diverse and informative responses, which is referred to as “safe response" problem BIBREF10 . To address this issue, we propose a new paradigm, prototype-then-edit, for response generation. Our motivations include: 1) human-written responses, termed as “prototypes response", are informative, diverse and grammatical which do not suffer from short and generic issues. Hence, generating responses by editing such prototypes is able to alleviate the “safe response" problem. 2) Some retrieved prototypes are not relevant to the current context, or suffer from a privacy issue. The post-editing process can partially solve these two problems. 3) Lexical differences between contexts provide an important signal for response editing. If a word appears in the current context but not in the prototype context, the word is likely to be inserted into the prototype response in the editing process. Inspired by this idea, we formulate the response generation process as follows. Given a conversational context INLINEFORM0 , we first retrieve a similar context INLINEFORM1 and its associated response INLINEFORM2 from a pre-defined index, which are called prototype context and prototype response respectively. Then, we calculate an edit vector by concatenating the weighted average results of insertion word embeddings (words in prototype context but not in current context) and deletion word embeddings (words in current context but not in prototype context). After that, we revise the prototype response conditioning on the edit vector. We further illustrate how our idea works with an example in Table TABREF1 . It is obvious that the major difference between INLINEFORM3 and INLINEFORM4 is what the speaker eats, so the phrase “raw green vegetables" in INLINEFORM5 should be replaced by “desserts" in order to adapt to the current context INLINEFORM6 . We hope that the decoder language model could remember the collocation of “desserts" and “bad for health", so as to replace “beneficial" with “bad" in the revised response. The new paradigm does not only inherits the fluency and informativeness advantages from retrieval results, but also enjoys the flexibility of generation results. Hence, our edit-based model is better than previous retrieval-based and generation-based models. The edit-based model can solve the “safe response" problem of generative models by leveraging existing responses, and is more flexible than retrieval-based models, because it does not highly depend on the index and is able to edit a response to fit current context. Prior work BIBREF11 has figured out how to edit prototype in an unconditional setting, but it cannot be applied to the response generation directly. In this paper, we propose a prototype editing method in a conditional setting. Our idea is that differences between responses strongly correlates with differences in their contexts (i.e. if a word in prototype context is changed, its related words in the response are probably modified in the editing.). We realize this idea by designing a context-aware editing model that is built upon a encoder-decoder model augmented with an editing vector. The edit vector is computed by the weighted average of insertion word embeddings and deletion word embeddings. Larger weights mean that the editing model should pay more attention on corresponding words in revision. For instance, in Table TABREF1 , we wish words like “dessert", “Tofu" and “vegetables" get larger weights than words like “and" and “ at". The encoder learns the prototype representation with a gated recurrent unit (GRU), and feeds the representation to a decoder together with the edit vector. The decoder is a GRU language model, that regards the concatenation of last step word embedding and the edit vector as inputs, and predicts the next word with an attention mechanism. Our experiments are conducted on a large scale Chinese conversation corpus comprised of 20 million context-response pairs. We compare our model with generative models and retrieval models in terms of fluency, relevance, diversity and originality. The experiments show that our method outperforms traditional generative models on relevance, diversity and originality. We further find that the revised response achieves better relevance compared to its prototype and other retrieval results, demonstrating that the editing process does not only promote response originality but also improve the relevance of retrieval results. Our contributions are listed as follows: 1) this paper proposes a new paradigm, prototype-then-edit, for response generation; 2) we elaborate a simple but effective context-aware editing model for response generation; 3) we empirically verify the effectiveness of our method in terms of relevance, diversity, fluency and originality.

Question: Which dataset do they evaluate on?
Answer: Chinese dataset containing human-human context response pairs collected from Douban Group