Task-specific Objectives of Pre-trained Language Models for Dialogue Adaptation

Pre-trained Language Models (PrLMs) have been widely used as backbones in lots of Natural Language Processing (NLP) tasks. The common process of utilizing PrLMs is first pre-training on large-scale general corpora with task-independent LM training objectives, then fine-tuning on task datasets with task-specific training objectives. Pre-training in a task-independent way enables the models to learn language representations, which is universal to some extent, but fails to capture crucial task-specific features in the meantime. This will lead to an incompatibility between pre-training and fine-tuning. To address this issue, we introduce task-specific pre-training on in-domain task-related corpora with task-specific objectives. This procedure is placed between the original two stages to enhance the model understanding capacity of specific tasks. In this work, we focus on Dialogue-related Natural Language Processing (DrNLP) tasks and design a Dialogue-Adaptive Pre-training Objective (DAPO) based on some important qualities for assessing dialogues which are usually ignored by general LM pre-training objectives. PrLMs with DAPO on a large in-domain dialogue corpus are then fine-tuned for downstream DrNLP tasks. Experimental results show that models with DAPO surpass those with general LM pre-training objectives and other strong baselines on downstream DrNLP tasks

Topic-Aware Multi-turn Dialogue Modeling

In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances. As a conversation goes on, topic shift at discourse-level naturally happens through the continuous multi-turn dialogue context. However, all known retrieval-based systems are satisfied with exploiting local topic words for context utterance representation but fail to capture such essential global topic-aware clues at discourse-level. Instead of taking topic-agnostic n-gram utterance as processing unit for matching purpose in existing systems, this paper presents a novel topic-aware solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way, so that the resulted model is capable of capturing salient topic shift at discourse-level in need and thus effectively track topic flow during multi-turn conversation. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each topic segment with the response in a dual cross-attention way. Experimental results on three public datasets show TADAM can outperform the state-of-the-art method, especially by 3.3% on E-commerce dataset that has an obvious topic shift

Multi-grained Evidence Inference for Multi-choice Reading Comprehension

Multi-choice Machine Reading Comprehension (MRC) is a major and challenging task for machines to answer questions according to provided options. Answers in multi-choice MRC cannot be directly extracted in the given passages, and essentially require machines capable of reasoning from accurate extracted evidence. However, the critical evidence may be as simple as just one word or phrase, while it is hidden in the given redundant, noisy passage with multiple linguistic hierarchies from phrase, fragment, sentence until the entire passage. We thus propose a novel general-purpose model enhancement which integrates multi-grained evidence comprehensively, named Multi-grained evidence inferencer (Mugen), to make up for the inability. Mugen extracts three different granularities of evidence: coarse-, middle- and fine-grained evidence, and integrates evidence with the original passages, achieving significant and consistent performance improvement on four multi-choice MRC benchmarks.Comment: Accepted by TASLP 2023, vol. 31, pp. 3896-390