Polite Task-oriented Dialog Agents: To Generate or to Rewrite? (2022)

Abstract: For task-oriented dialog agents, the tone of voice mediates user-agent interactions, playing a central role in the flow of a conversation. Distinct from domain-agnostic politeness constructs, in specific domains such as online stores, booking platforms, and others, agents need to be capable of adopting highly specific vocabulary, with significant impact on lexical and grammatical aspects of utterances. Then, the challenge is on improving utterances’ politeness while preserving the actual content, an utterly central requirement to achieve the task goal. In this paper, we conduct a novel assessment of politeness strategies for task-oriented dialog agents under a transfer learning scenario. We extend existing generative and rewriting politeness approaches, towards overcoming domain-shifting issues, and enabling the transfer of politeness patterns to a novel domain. Both automatic and human evaluation is conducted on customer-store interactions, over the fashion domain, from which contribute with insightful and experimentally supported lessons regarding the improvement of politeness in task-specific dialog agents.

## introduction in a conversational scenario, the tone of voice used by interlocutors is a key aspect towards achieving fruitful, engaging, and natural user-agent interactions @xcite . this is deeply rooted in the fact that discoursing in a polite manner, is a social trait of human conversations, that when left unattended by dialog agents, can lead to an immediate perception of artificial discourse and lack of intelligent behavior, which in turn leads to poor engagement. task-oriented dialog agents require simultaneously keeping the user engaged while achieving the task goal, whether it is selling a product, booking a restaurant or simply providing assistance. this requires informative and correct answers, embedding in this example the politeness strategy adopted is the use of a positive lexicon. while politeness is a deeply seeded cultural concept and difficult to fully generalize @xcite , it has been recently approached from a computational perspective @xcite @xcite under the framework of @xcite , which divides politeness strategies in a) negative politeness -where polite discourse is achieved by expressing restraint, thus avoiding being direct -and b) positive politeness -where an explicit attempt of expressing solidarity, optimism and gratitude is made. @xcite took a pioneering approach and proposed to approximate these strategies by creating a human annotated politeness corpora, and training a classifier to capture general linguistic patterns of both negative and positive politeness. recent works leverage on such classifier to develop either generative @xcite or rewritingbased @xcite ) approaches. figure 1 contrasts these approaches, with respect to politeness strategies. while these have been applied to generic and domain-agnostic scenarios, it remains unclear how well such principles transfer to task-specific domains. in this work, we assess under a transfer-learning scenario, the applicability of both generative and rewriting politeness approaches to a novel domain. specifically, we use the challenging fashion domain as a use case, given its vocabulary complexity and highly specific-nature foot_0 . namely, we propose to overcome the lack of labeled data by extending state-of-the-art generative @xcite and rewriting @xcite approaches, respectively, towards allowing each of them to overcome the domain-shift, and transferring linguistic politeness constructs to a novel (fashion) domain. this is one of the first works to study politeness approaches for task-oriented dialog agents, contributing with: ## related work the importance of politeness in social interactions and its impact in the projected self-image during social interactions has been studied for decades @xcite . these concepts were later reviewed and refined @xcite with new work @xcite proposing the label of 'polite behavior' to separate it from the theoretical and cultural baggage of the term facework. more recently, danescu-niculescu-mizil et al. ( 2013 ) introduced a labeled dataset (stanford politeness corpus), along with a politeness classifier to enable further research as an nlp task. additionally, they look into how politeness relates to the speaker's status and power within their community. later work @xcite introduced a new politeness classifier and several visualization techniques to gain further insight into linguistic markers of politeness. these visualization techniques reveal novel politeness strategies not considered originally, namely how punctuation affected politeness scores. the introduced politeness classifier uses a cnn and does not use politeness strategies as features while having higher accuracy. politeness as an nlp task has seen recent interest. @xcite uses the stanford politeness corpus to investigate politeness generation models. politeness generation here is treated as part of the answer generation task with models producing answers already in their polite form, using reinforcement learning and a novel politeness classifier. a multilingual approach is taken in @xcite where courteous responses are generated in a customer care scenario. @xcite sees politeness as a style transfer task where politeness is introduced onto an utterance by rewriting it. this work uses a politeness classifier to label the enron corpus @xcite , and applies a transformer-based @xcite style transfer pipeline to the utterance, using a tagger and generator approach. in a similar vein, in @xcite the authors transform neutral customer service replies into courteous ones. hence, we follow a similar line of work and propose to enrich fashion dialog agents with politeness. @xcite introduced a large-scale multimodal fashion dialog dataset (mmd) built semi-automatically, using field experts, accompanied by two rnn @xcite models capable of emulating the system responses in a multimodal scenario. due to its domain, it carries mainly neutral and polite dialog. to the best of our knowledge, there is no task-oriented conversational dataset to study politeness and we propose to fill this research gap. ## task-specific polite dialog agents we consider two distinct methods of producing politeness and evaluate how each deals with domain changes: polite answer generation and politeness rewriting. we adapt each model to allow it to use transfer learning, in particular, transfer politeness patterns to a different domain, the fashion domain. ## conclusion in this work, we address the research gap regarding the development of polite task-oriented dialog agents. we demonstrate that while politeness language constructs tend not to be domain-specific, their application is, requiring politeness approaches to cope with domain-specific vocabulary. particularly, we show that when improving politeness in task-specific utterances, rewriting approaches con-sistently deliver better results, given that generative alternatives need to attend to two tasks. in summary, the key takeaways are: as future work, we plan to extend our work and research methods that select the best politeness strategies while accounting for the specificity of distinct conversation phases (e.g. greeting vs. product description utterances). more repetitions of the same token, a token favored by the politeness classifier. still considering the bleu scores, model 1 presented some surprising results as we were not expecting text degeneration to also occur in this scenario, but it did, albeit not as accentuated as in the later 2 models. when taking into account the politeness scores, we see that increasing the beta value clearly improves politeness generation, or so it would seem, as mentioned before all of the models that beat the reference in politeness generation did it by starting to generate the same token repeatedly mid-sentence. these results showed us that when trying to encourage politeness generation, we cannot solely rely on token probability distributions, semantics need to be taken into account or, at least, vocabulary diversity at the classifier level, since any other way the model is not punished by simply outputting the classifier's favorite word, 'belt' in some cases and 'republic' in others. this means that, using the polite-rl, the best balance that can be achieved results is a compromise in generation quality while not making the used tone polite. for conversational agents, this is important as the question answering quality needs to remain high throughout the conversation.