Consistent Response Generation with Controlled Specificity (2020)

Abstract: We propose a method to control the specificity of responses while maintaining the consistency with the utterances. We first design a metric based on pointwise mutual information, which measures the co-occurrence degree between an utterance and a response. To control the specificity of generated responses, we add the distant supervision based on the co-occurrence degree and a PMI-based word prediction mechanism to a sequence-to-sequence model. With these mechanisms, our model outputs the words with optimal specificity for a given specificity control variable. In experiments with open-domain dialogue corpora, automatic and human evaluation results confirm that our model controls the specificity of the response more sensitively than the conventional model and can generate highly consistent responses.

## introduction open-domain response generation is a task for generating a human-like responses to chit-chatting. there are many end-to-end response generation models @xcite @xcite that apply a sequence-to-sequence (seq2seq) @xcite architecture, which allows the generation of fluent responses. however, the seq2seq model suffers from a tendency to generate safe but overly typical responses (i.e. dull responses), such as "yes" and "i don't understand." to solve this problem, several studies proposed methods to increase the specificity of the generated responses @xcite @xcite ; however, simply maximizing the specificity of the response results in a degenerative solution that generates a specific but inconsistent responses. in this study, we define the conditions that an automatically generated response is expected to satisfy as (i) being consistent with an input utterance, (ii) being specific to provide informative contents, and (iii) being controllable. as shown in figure 1 , in a human conversation, an utterance could have various responses with different specificity @xcite . then, humans control the specificity of the response as necessary. thus, instead of only generating highly specific responses, the specificity should be controllable in response generation tasks. we propose a method to control the specificity of responses while maintaining their consistency with the utterances. following the observation that a response uniquely co-occurring with a specific utterance in a corpus is both specific and consistent for the utterance, we design a metric called maxpmi, which measures the co-occurrence degree between an utterance and a response on the basis of positive pointwise mutual information (ppmi). we apply the distant supervision into our model using automatically annotated maxpmi scores of the training set. at the inference, the specificity of the generated responses can be controlled by inputting a desired specificity level. we also propose a method to automatically set the specificity level by estimating the maximum maxpmi score for an input utterance, which allows the generation of a response which has the maximum mutual information with the input. we conducted both automatic and human eval-uations using dailydialog and twitter corpora. the results confirmed that our method largely outperformed the methods in previous studies and achieved sensitive control of the specificity of the output responses. ## related work previous studies focus on addressing the dull response problem generated by seq2seq models. li et al. (2016a) rerank the n -best generated responses using an objective function to maximize the mutual information between the utterance and generated sentences. because this method is postprocessing, it ceases to be effective if there are no appropriate response candidates among the nbest responses. to directly improve the specificity of each response generated, previous studies devised training mechanisms of seq2seq models by penalising for the generation of dull responses and eventually training models to generate specific responses. yao et al. (2016) and li et al. (2016b) apply reinforcement learning, and xu et al. (2017) and zhang et al. (2018b) apply generative adversarial networks, to directly generate specific responses. based on the hypothesis that the specificity of sentences increases with the number of low-frequency words, nakamura et al. (2019) and jiang et al. (2019) propose loss functions weighted by word frequency. in contrast, to ensure both specificity and consistency, takayama and arase @xcite propose a model that directly promotes the generation of words that co-occur with uttered sentences on the basis of ppmi. their model includes a mechanism for deciding whether or not to generate words of high co-occurrence with the utterance at each decoding step. in this study, we apply this method to our model for proactively generating specific words in a response. controlling the properties of generated responses is also related to our study. @xcite and @xcite allow for the control of dialogue-acts, length, and specificity of responses; however, they are resource intensive and thus require an external annotated corpus. in contrast, sc-seq2seq @xcite achieves control of response specificity without dependence on external resources, which is most relevant to our study. moreover, sc-seq2seq applies distant supervision, but uses word frequency in responses as a measure of specificity. at inference, sc-seq2seq requires to input a desired specificity realized in the response. we measure specificity based on ppmi between an utterance and response, hence, our method can maintain both specificity and consistency to the utterance. additionally, our method can estimate the maximum specificity for each input utterance, and automatically adjust the specificity of generated responses. ## proposed method the proposed method is depicted in figure 2 . in the proposed method, first, a label that indicates the cooccurrence degree between utterance and response is automatically annotated by maxpmi score (section 3.1). the model generates sentences on the basis of previously calculated ppmi and maxpmi (see section 3.2). the training is performed using the framework of distant supervision based on the utterance-response pair and the maxpmi score given beforehand (section 3.3). at the inference, responses are generated using one method of inputting a manually determined specificity level or automatically estimated specificity level considering the input utterance (see section 3.4). since we aim to explicitly control the amount of information in response to utterances, we use the decoder architecture of @xcite which has an output gating mechanism that controls whether or not to generate specific words at each decoding time-step. ## experimental settings to evaluate whether our model can control the specificity of the responses while maintaining their consistency with the utterances, we conducted response-generation experiments using japanese and english chit-chat dialogue corpora. ## conclusion we empirically showed that the co-occurrence relationship between words in an utterance and words in its response helps to control the specificity in response generation. the conventional specificity control model often generates responses with less consistency with the utterances. in contrast, our model can control specificity of the responses while maintaining the consistency with the utterance. as future work, we shall improve the proposed method to maintain the fluency in responses by addressing the repeated word problem. further, an appropriate specificity level of a response depends on the previous utterances and responses, i.e. conversation systems that always return highly specific responses are annoying. hence, we intend to propose a method to adjust the specificity level considering the conversation history.