Fine-grained Conversational Decoding via Isotropic and Proximal Search (2023)

Abstract: General-purpose text decoding approaches are usually adopted for dialogue response generation. Although the quality of the generated responses can be improved with dialogue-specific encoding methods, conversational decoding methods are still under-explored. Inspired by SimDRC that a good dialogue feature space should follow the rules of locality and isotropy, we present a fine-grained conversational decoding method, termed isotropic and proximal search (IPS). Our method is designed to generate the semantic-concentrated response, while still maintaining informativeness and discrimination against the context. Experiments show that our approach significantly outperforms existing decoding strategies in the dialogue field across both automatic and human evaluation metrics. More in-depth analyses further confirm the effectiveness of our approach.

## introduction dialogue response generation @xcite aims to generate the utterance that forms a coherent and fluent continuation given a dialogue context. generic text decoding strategies @xcite @xcite are usually adopted to produce grammatical and contextual responses. as an independent technique, decoding strategy can also enhance the generation quality of large language models. existing text decoding methods have been explored in various generic text generation tasks, but lack tailoring for dialogue generation, e.g., capturing dialogue-specific features and generating an informative and discriminative dialogue response @xcite . early maximizationbased methods, e.g., greedy search @xcite and beam search @xcite , may lead to dullness and degeneration @xcite . later sampling-based improvements are proposed to tackle these problems, including top-k sampling @xcite and nucleus search @xcite . while alleviating degeneration, these sampling methods introduce critical semantic inconsistency and are not aligned with human-written prefix @xcite . specifically, a bunch of studies @xcite have asserted that the problem of anisotropy, i.e., a distribution pattern in the latent space with features occupying a narrow cone in the space, leads to inconsistency and degradation of the generation. although contrastive search @xcite has been proposed correspondingly to mitigate the issue, as a generalized text decoding strategy, it still ignores dialoguespecific features, such as utterance dependencies and conversational structure information. therefore, research on conversational decoding methods is warmly needed. in this work, we propose a fine-grained conversational decoding method, namely isotropic and proximal search (ips). different from traditional approaches, we consider the previous tokens and contexts separately from a granular perspective. acknowledging that locality and isotropy are two important properties for refining the dialogue feature space, we design our ips following these rules: (i) the generated output should be selected from the most probable candidate set predicted by the dialogue model; (ii) the generated tokens in the same utterance should be proximal to each other for expressing a concentrated idea; and (iii) the newly generated utterance should be discriminative enough with respect to the context utterances. in this way, our method encourages informativeness and discrimination among different utterances as well as maintains a concentrated idea within an utterance. we evaluate our approach on two commonly-used dialogue datasets, dailydialog @xcite in english and lccc @xcite in chinese. both human and automatic evaluation results, i.e., indicators based on gpt3.5, consistently show that ips can generate more fluent, coherent, and human-like responses than existing decoding methods. ## experiments dataset we evaluate our method on two commonly-used datasets, dailydialog @xcite in english and lccc @xcite in chinese. both of them are open-domain multi-turn dialogue datasets, collected from social media. for lccc, owing to the academic-level computing resource, we follow previous work @xcite , and sample a subset of the dataset, consisting of 100,000 dialogue examples. baselines following @xcite , we use bart @xcite as our backbone. we evaluate the performance of decoding strategies with different models, including vanilla bart, bart with simctg @xcite , and bart with simdrc @xcite . we compare ips to greedy search, beam search, top-k sampling @xcite , nucleus sampling @xcite and contrastive search @xcite . settings we fine-tune the models on dailydialog and lccc datasets for 6k steps and 7k steps, respectively. we use a batch size of 64 and truncate the training samples to a maximum length of 256. the parameters of the models are initialized from huggingface libraries and updated by adam optimizer (kingma and ba, 2017) with a learning rate of 3e-5. we adopt the margin values of simctg and simdrc suggested in their work, i.e., ρ = 0.5 for simctg and δ = 0.7, α = 0.3 for simdrc. we conduct the isotropic and proximal search with the first @xmath0. the weight α is 0.6. we run all experiments with five different seeds and report the average score. evaluation metrics traditional n-gram overlap and text matching metrics such as bleu @xcite and rouge @xcite are not proper to evaluate plausible output diversity for open-domain dialog systems. therefore, for automatic evaluation, we choose the following metrics, including bertscore @xcite ), 1 2 3 4 5 the value of n 2 2.1 2.2 2.3 2.4 2.5 g-eval simdrc+ips simctg+ contrastive search (a) ablation on the first n steps 4 5 6 7 8 the value of k 2 2.1 2.2 2.3 2.4 2.5 g-eval simdrc+ips simctg+contrastive search (b) ablation on top-k sampling 0.4 0.5 0.6 0.7 0.8 the value of 2.1 2.2 2.3 2.4 2.5 g-eval simdrc+ips simctg+contrastive search (c) ablation on the hyperparameter α mauve @xcite , distinct2/4 @xcite , and g-eval, an automatic evaluation metric based on gpt3.5 @xcite . we also conduct a human evaluation with the help of recruited proficient english/chinese speakers. we randomly sample 100 dialogue examples from dailydialog and lccc test sets. for each dialogue context, we generate responses using the aforementioned backbone models (bart, bart+simctg, bart+simdrc) with six different inference strategies. five annotators are hired independently to measure these samples. annotators are instructed to give a score ranging from 1 to 5 over the following aspects, including fluency, informativeness, coherence, and semantic coverage 1 . ## conclusion in this work, we present a fine-grained conversational decoding strategy, namely isotropic and proximal search (ips) to encourage the generation of isotropic and conversational tokens. superior to existing decoding methods, ips decouples the previous tokens and the context. experiments show that our method achieves impressive performance on both automatic and human evaluation.