H e L o: Learning-Free Lookahead Decoding for Conversation Infilling (2022)

Abstract: We propose Heuristic Guided Lookahead Decoding (HeLo), a novel decoding strategy for conversation infilling. Conversation infilling aims to generate a seamless bridge of utterances connecting a given pair of source and target utterances. HeLo does not require fine-tuning or extra models – only the generating model itself. Instead, HeLo leverages a greedy lookahead phase before committing to any token. The HeLo framework is simple and can augment conventional decoding strategies paired with any autoregressive language model. Smooth transitions between utterances are encouraged with an annealing schedule. Our experiments show HeLo outperforms several baselines when evaluated with both automatic and human evaluation metrics, which, we argue, are appropriate for the task.

## introduction large pretrained language models are effective solutions to many popular natural language generation tasks such as machine translation and conversational dialogue. guided content generation, however, is an equally compelling application that has received relatively less attention. in this setting, humans cooperate with language models to produce works of creative writing such as stories @xcite . in this paper, we explore the cooperative generation of conversations which we dub conversation infilling. conversation infilling aims to generate a seamless bridge of utterances connecting a given pair of source and target utterances. such a task finds itself in many forms of creative writing, such as playwriting, movie scripts, and video game dialogue. for example, production of video game dialogue is large in scale. game worlds often contain more interactive characters than a writer could ever hope to compose unique dialogues for. we envision conversation infilling as a scalable method to generate conversations where writers control the high-level aspects of conversations while relying on an ai-assisted writing tool to fill in the details. while similar to the task of text infilling @xcite , conversation infilling requires explicitly the generation of an entire dialogue between two interlocutors rather than arbitrary text. we propose a heuristic guided lookahead decoding strategy (helo, pronounced "hello!") for the task of conversation infilling. helo does not require fine-tuning or additional models outside the generating model itself. instead, before committing to any token, helo performs greedy lookaheads to generate potential future conversations and prioritizes tokens that bring the conversation closer to the target utterance with a heuristic scoring function. to encourage smooth transitions between utterances, the magnitude of this heuristic bias depends on the current depth of the conversation. we compare helo against several baselines across five datasets and propose a diverse set of automatic evaluation metrics, which, when taken together, are reasonable for our task. our experiments demonstrate that helo outperforms all baselines on the majority of these metrics, albeit at the cost of generation speed. while speed is vital in real-time settings such as chitchat, we contend that it is fair to perform conversation infilling in an offline setting where speed holds a lower priority. we also perform a small human evaluation study that suggests helo is a promising approach to conversation infilling. ## conversation infilling with helo given a pair of source and target utterances y src and y tgt , the task of conversation infilling is to generate an l length sequence of utterances (y (1) , y (2) , . . . , y (l) ) that coherently bridges y src and y tgt . in other words, any utterance y (i) in the sequence (y src , y (1) , y (2) , . . . , y (l) , y tgt ) is a sensible response to its dialogue history (y src , y (1) , ..., y (i-1) ). we show an example of conversation infilling in figure 1 . in formal terms, we wish to solve arg max y (1) ...y (l) p(y ## experimental setup model choice. we use blenderbot @xcite as our backbone language model for all experiments. specifically, we use the 400m-distill checkpoint. datasets. we run our experiments over five datasets: blendedskilltalk (smith et al., 2020), empatheticdialogues @xcite , wizard of wikipedia @xcite , personachat @xcite , and meena @xcite . all datasets are composed of english human-to-human conversations filtered such that each conversation contains 6 to 8 utterances. see appendix e for details. utterance perplexity (ppl x ) measures the perplexity of an utterance with respect to its dialogue history. we use blenderbot 1b-distill throughout our experiments to compute perplexity. given a conversation, ppl max is the perplexity of the most perplexing utterance of a conversation. ppl y (1) and ppl y tgt are the perplexities of y (1) and y tgt , respectively. a low utterance perplexity suggests a sensible and fluent response. ## related work while, to the best of our knowledge, we are the first to explore conversation infilling, many have explored the related tasks of text infilling @xcite @xcite and controllable text generation @xcite @xcite . closer to conversation infilling, @xcite propose a method to guide conversations towards a target keyword. @xcite explore the task of proactive conversation where a dialogue agent leads a conversation by planning over a knowledge graph. @xcite and @xcite explore the task of one-turn topic transitions: given a source utterance u a and a partial utterance u b , generate text u ′ b such that the concatenation of u ′ b and u b is a sensible response to u a . conversation infilling, in contrast, requires the generation of an entire conversation that bridges two utterances on behalf of both speakers. moreover, their proposed methods require fine-tuning and external knowledge bases, while helo is a learning-free decoding method. @xcite propose neurologic a*esque (nl), a decoding method that also employs a lookahead phase. the main differences between our ## conclusion we propose helo, a learning-free heuristic guided decoding strategy for the task of conversation infilling. automatic and human experiments suggest helo is a viable strategy compared to several baselines. future work of interest includes improving the generation speed of helo for use in real-time settings and exploring other natural language tasks that may benefit from lookahead heuristics.