Learning to Plan and Realize Separately for Open-Ended Dialogue Systems (2020)

Abstract: Achieving true human-like ability to conduct a conversation remains an elusive goal for open-ended dialogue systems. We posit this is because extant approaches towards natural language generation (NLG) are typically construed as end-to-end architectures that do not adequately model human generation processes. To investigate, we decouple generation into two separate phases: planning and realization. In the planning phase, we train two planners to generate plans for response utterances. The realization phase uses response plans to produce an appropriate response. Through rigorous evaluations, both automated and human, we demonstrate that decoupling the process into planning and realization performs better than an end-to-end approach.

## introduction recent advancements in the area of generative modeling have helped increase the fluency of generative models. however, several issues persist: coherence of output and the semblance of mere repetition/hallucination of tokens from the training data @xcite . one reason could be that the generation task is typically construed as an end-to-end system. this is in contrast to traditional approaches, which incorporate a sequence of steps in the nlg system, including content determination, sentence planning, and surface realization @xcite . a review of literature from psycholinguistics and cognitive science also provides strong empirical evidence that the human language production process is not a monolith @xcite @xcite . prior approaches have indeed incorporated content planning into the nlg system, for example data-to-text generation problems @xcite as well as classic works that include planning, based on speech acts @xcite (for an in-depth review c.f. @xcite ). our work closely follows these prior approaches, with one crucial difference: our planners are not based on dialogue acts or speech acts. consider the example in fig. 1 . an input utterance by person b, a statement (unfortunately no.), followed by a question (what do they do?), can be effectively responded to using plans, learned and generated, prior to the realization phase. the realization output can then include the mention of provides relief, consistent with the generated plan @xcite ]). dialogue acts @xcite (e.g., statements, questions), by their nature, encompass a wide variety of realized output, and hence cannot sufficiently constrain the language model during the generation process. research has addressed this issue by adapting existing taxonomies @xcite towards their own goals @xcite . we instead use an adapted and extended form of lexical-conceptual structures (lcss) to help constrain the realization output more effectively @xcite . our work makes the following contributions: • we investigate the impact of separating planning and realization in open-domain dialogue and find that the approach produces better responses per automated metrics and detailed human evaluations. ## evaluation of approach the results reported in these subsections were obtained by combining both corpora and dividing randomly in a ratio of 80/10/10 for the training, testing, and validation set. ## conclusion and future work we address the task of natural language generation in open-ended dialogue systems. we test our hypothesis that decoupling the generation process into planning and realization can achieve better performance than an end-to-end approach. in the planning phase, we explore three methods to generate response plans, including a symbolic planner and two learned planners, the context attention and pseudo self attention models. through linguist expert evaluation, we are able to determine the efficacy of the response plans towards realization. in the realization phase, we use the pseudo self attention model to make use of the learned response plans to generate responses. our key finding through two separate human crowdsourced studies is that decoupling realization, and planning phases outperforms an endto-end no planner system across three metrics (appropriateness, quality, and usefulness). in this work, we have taken an initial step towards the goal of replicating human language generation processes. thorough and rigorous evaluations are required to fully support our claims, e.g., by including additional metrics and more diverse corpora. in this work, we limit the types to give, gain, lose, and perform. however, we do not restrict the ask action and target at all. also, since our symbolic planner can be used to obtain silver standard training data, straightforward changes like adding additional lexicons would enable us to generalize to other corpora as well as include additional ask types in our pipeline. another natural extension would be to explore training the planning and realization phases together in a hierarchical process @xcite . this would, in principle, further validate the efficacy of our approach.