Filling the Gap of Utterance-aware and Speaker-aware Representation for
Multi-turn Dialogue
- URL: http://arxiv.org/abs/2009.06504v2
- Date: Sat, 12 Dec 2020 19:01:56 GMT
- Title: Filling the Gap of Utterance-aware and Speaker-aware Representation for
Multi-turn Dialogue
- Authors: Longxiang Liu, Zhuosheng Zhang, Hai Zhao, Xi Zhou, Xiang Zhou
- Abstract summary: A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles.
In the existing retrieval-based multi-turn dialogue modeling, the pre-trained language models (PrLMs) as encoder represent the dialogues coarsely.
We propose a novel model to fill such a gap by modeling the effective utterance-aware and speaker-aware representations entailed in a dialogue history.
- Score: 76.88174667929665
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A multi-turn dialogue is composed of multiple utterances from two or more
different speaker roles. Thus utterance- and speaker-aware clues are supposed
to be well captured in models. However, in the existing retrieval-based
multi-turn dialogue modeling, the pre-trained language models (PrLMs) as
encoder represent the dialogues coarsely by taking the pairwise dialogue
history and candidate response as a whole, the hierarchical information on
either utterance interrelation or speaker roles coupled in such representations
is not well addressed. In this work, we propose a novel model to fill such a
gap by modeling the effective utterance-aware and speaker-aware representations
entailed in a dialogue history. In detail, we decouple the contextualized word
representations by masking mechanisms in Transformer-based PrLM, making each
word only focus on the words in current utterance, other utterances, two
speaker roles (i.e., utterances of sender and utterances of receiver),
respectively. Experimental results show that our method boosts the strong
ELECTRA baseline substantially in four public benchmark datasets, and achieves
various new state-of-the-art performance over previous methods. A series of
ablation studies are conducted to demonstrate the effectiveness of our method.
Related papers
- WavChat: A Survey of Spoken Dialogue Models [66.82775211793547]
Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain.
These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech.
Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems.
arXiv Detail & Related papers (2024-11-15T04:16:45Z) - SPECTRUM: Speaker-Enhanced Pre-Training for Long Dialogue Summarization [48.284512017469524]
Multi-turn dialogues are characterized by their extended length and the presence of turn-taking conversations.
Traditional language models often overlook the distinct features of these dialogues by treating them as regular text.
We propose a speaker-enhanced pre-training method for long dialogue summarization.
arXiv Detail & Related papers (2024-01-31T04:50:00Z) - Pre-training Multi-party Dialogue Models with Latent Discourse Inference [85.9683181507206]
We pre-train a model that understands the discourse structure of multi-party dialogues, namely, to whom each utterance is replying.
To fully utilize the unlabeled data, we propose to treat the discourse structures as latent variables, then jointly infer them and pre-train the discourse-aware model.
arXiv Detail & Related papers (2023-05-24T14:06:27Z) - Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension [81.47133615169203]
We propose compositional learning for holistic interaction across utterances beyond the sequential contextualization from PrLMs.
We employ domain-adaptive training strategies to help the model adapt to the dialogue domains.
Experimental results show that our method substantially boosts the strong PrLM baselines in four public benchmark datasets.
arXiv Detail & Related papers (2023-01-10T13:18:25Z) - Who says like a style of Vitamin: Towards Syntax-Aware
DialogueSummarization using Multi-task Learning [2.251583286448503]
We focus on the association between utterances from individual speakers and unique syntactic structures.
Speakers have unique textual styles that can contain linguistic information, such as voiceprint.
We employ multi-task learning of both syntax-aware information and dialogue summarization.
arXiv Detail & Related papers (2021-09-29T05:30:39Z) - Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension [43.352833140317486]
Multi-party multi-turn dialogue comprehension brings unprecedented challenges.
Most existing methods deal with dialogue contexts as plain texts.
We propose an enhanced speaker-aware model with masking attention and heterogeneous graph networks.
arXiv Detail & Related papers (2021-09-09T07:12:22Z) - Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance
for Multi-party Dialogue Reading Comprehension [46.69961067676279]
Multi-party dialogue machine reading comprehension (MRC) brings tremendous challenge since it involves multiple speakers at one dialogue.
Previous models focus on how to incorporate speaker information flows using complex graph-based modules.
In this paper, we design two labour-free self- and pseudo-self-supervised prediction tasks on speaker and key-utterance to implicitly model the speaker information flows.
arXiv Detail & Related papers (2021-09-08T16:51:41Z) - Dialogue-Based Relation Extraction [53.2896545819799]
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE.
We argue that speaker-related information plays a critical role in the proposed task, based on an analysis of similarities and differences between dialogue-based and traditional RE tasks.
Experimental results demonstrate that a speaker-aware extension on the best-performing model leads to gains in both the standard and conversational evaluation settings.
arXiv Detail & Related papers (2020-04-17T03:51:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.