Exophoric Pronoun Resolution in Dialogues with Topic Regularization
- URL: http://arxiv.org/abs/2109.04787v1
- Date: Fri, 10 Sep 2021 11:08:31 GMT
- Title: Exophoric Pronoun Resolution in Dialogues with Topic Regularization
- Authors: Xintong Yu, Hongming Zhang, Yangqiu Song, Changshui Zhang, Kun Xu and
Dong Yu
- Abstract summary: Resolving pronouns to their referents has long been studied as a fundamental natural language understanding problem.
Previous works on pronoun coreference resolution (PCR) mostly focus on resolving pronouns to mentions in text while ignoring the exophoric scenario.
We propose to jointly leverage the local context and global topics of dialogues to solve the out-of-textPCR problem.
- Score: 84.23706744602217
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Resolving pronouns to their referents has long been studied as a fundamental
natural language understanding problem. Previous works on pronoun coreference
resolution (PCR) mostly focus on resolving pronouns to mentions in text while
ignoring the exophoric scenario. Exophoric pronouns are common in daily
communications, where speakers may directly use pronouns to refer to some
objects present in the environment without introducing the objects first.
Although such objects are not mentioned in the dialogue text, they can often be
disambiguated by the general topics of the dialogue. Motivated by this, we
propose to jointly leverage the local context and global topics of dialogues to
solve the out-of-text PCR problem. Extensive experiments demonstrate the
effectiveness of adding topic regularization for resolving exophoric pronouns.
Related papers
- Multi-turn Dialogue Comprehension from a Topic-aware Perspective [70.37126956655985]
This paper proposes to model multi-turn dialogues from a topic-aware perspective.
We use a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way.
We also present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements.
arXiv Detail & Related papers (2023-09-18T11:03:55Z) - Multi-Granularity Prompts for Topic Shift Detection in Dialogue [13.739991183173494]
The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change.
Previous work focused on detecting topic shifts using pre-trained models to encode the utterance.
We take a prompt-based approach to fully extract topic information from dialogues at multiple-granularity, i.e., label, turn, and topic.
arXiv Detail & Related papers (2023-05-23T12:35:49Z) - Enhancing Dialogue Summarization with Topic-Aware Global- and Local-
Level Centrality [24.838387172698543]
We propose a novel topic-aware Global-Local Centrality (GLC) model to help select the salient context from all sub-topics.
The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic.
Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets.
arXiv Detail & Related papers (2023-01-29T06:41:55Z) - Question-Interlocutor Scope Realized Graph Modeling over Key Utterances
for Dialogue Reading Comprehension [61.55950233402972]
We propose a new key utterances extracting method for dialogue reading comprehension.
It performs prediction on the unit formed by several contiguous utterances, which can realize more answer-contained utterances.
As a graph constructed on the text of utterances, we then propose Question-Interlocutor Scope Realized Graph (QuISG) modeling.
arXiv Detail & Related papers (2022-10-26T04:00:42Z) - VD-PCR: Improving Visual Dialog with Pronoun Coreference Resolution [79.05412803762528]
The visual dialog task requires an AI agent to interact with humans in multi-round dialogs based on a visual environment.
We propose VD-PCR, a novel framework to improve Visual Dialog understanding with Pronoun Coreference Resolution.
With the proposed implicit and explicit methods, VD-PCR achieves state-of-the-art experimental results on the VisDial dataset.
arXiv Detail & Related papers (2022-05-29T15:29:50Z) - Pragmatic constraints and pronoun reference disambiguation: the possible
and the impossible [0.0]
In AI and linguistics research, this has mostly been studied in cases where the referent is explicitly stated in the preceding text nearby.
Pronouns in natural text often refer to entities, collections, or events that are only implicitly mentioned previously.
It is occasionally possible to have a pronoun that is far separated from its referent in a text.
arXiv Detail & Related papers (2022-04-03T21:57:58Z) - CorefDRE: Document-level Relation Extraction with coreference resolution [0.0]
We use mention-pronoun coreference information to represent multi-sentence features by pronouns.
A mention-pronoun coreference resolution is introduced to calculate the affinity between pronouns and corresponding mentions.
Experiments on the public dataset, DocRED, DialogRE and MPDD, show that Coref-aware Doc-level Relation Extraction based on Graph Inference Network outperforms the state-of-the-art.
arXiv Detail & Related papers (2022-02-22T09:03:59Z) - A Brief Survey and Comparative Study of Recent Development of Pronoun
Coreference Resolution [55.39835612617972]
Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to.
As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models.
We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications.
arXiv Detail & Related papers (2020-09-27T01:40:01Z) - Topic-Aware Multi-turn Dialogue Modeling [91.52820664879432]
This paper presents a novel solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way.
Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network.
arXiv Detail & Related papers (2020-09-26T08:43:06Z)
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.