Communicating with Speakers and Listeners of Different Pragmatic Levels
- URL: http://arxiv.org/abs/2410.05851v1
- Date: Tue, 8 Oct 2024 09:42:37 GMT
- Title: Communicating with Speakers and Listeners of Different Pragmatic Levels
- Authors: Kata Naszadi, Frans A. Oliehoek, Christof Monz,
- Abstract summary: This paper explores the impact of variable pragmatic competence on communicative success through simulating language learning.
We find that learning from more explicit, literal language is advantageous, irrespective of the learner's level of pragmatic competence.
- Score: 14.94138113774852
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper explores the impact of variable pragmatic competence on communicative success through simulating language learning and conversing between speakers and listeners with different levels of reasoning abilities. Through studying this interaction, we hypothesize that matching levels of reasoning between communication partners would create a more beneficial environment for communicative success and language learning. Our research findings indicate that learning from more explicit, literal language is advantageous, irrespective of the learner's level of pragmatic competence. Furthermore, we find that integrating pragmatic reasoning during language learning, not just during evaluation, significantly enhances overall communication performance. This paper provides key insights into the importance of aligning reasoning levels and incorporating pragmatic reasoning in optimizing communicative interactions.
Related papers
- Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations [22.56326809612278]
We present an evaluation framework for interactive dialogue assessment in the context of English as a Second Language speakers.
Our framework collects dialogue-level interactivity labels and micro-level span features.
We study how the micro-level features influence the (higher level) interactivity quality of ESL dialogues by constructing various machine learning-based models.
arXiv Detail & Related papers (2024-07-09T00:56:59Z) - Can LLMs Understand the Implication of Emphasized Sentences in Dialogue? [64.72966061510375]
Emphasis is a crucial component in human communication, which indicates the speaker's intention and implication beyond pure text in dialogue.
This paper introduces Emphasized-Talk, a benchmark with emphasis-annotated dialogue samples capturing the implications of emphasis.
We evaluate various Large Language Models (LLMs), both open-source and commercial, to measure their performance in understanding emphasis.
arXiv Detail & Related papers (2024-06-16T20:41:44Z) - Towards Harnessing Large Language Models for Comprehension of Conversational Grounding [1.8434042562191812]
This study investigates the capabilities of large language models in classifying dialogue turns related to explicit or implicit grounding and predicting grounded knowledge elements.
Our experimental results reveal challenges encountered by large language models in the two tasks.
These initiatives aim to develop more effective dialogue systems that are better equipped to handle the intricacies of grounded knowledge in conversations.
arXiv Detail & Related papers (2024-06-03T19:34:39Z) - From Heuristic to Analytic: Cognitively Motivated Strategies for
Coherent Physical Commonsense Reasoning [66.98861219674039]
Heuristic-Analytic Reasoning (HAR) strategies drastically improve the coherence of rationalizations for model decisions.
Our findings suggest that human-like reasoning strategies can effectively improve the coherence and reliability of PLM reasoning.
arXiv Detail & Related papers (2023-10-24T19:46:04Z) - From Multilingual Complexity to Emotional Clarity: Leveraging
Commonsense to Unveil Emotions in Code-Mixed Dialogues [38.87497808740538]
Understanding emotions during conversation is a fundamental aspect of human communication, driving NLP research for Emotion Recognition in Conversation (ERC)
We propose an innovative approach that integrates commonsense information with dialogue context to facilitate a deeper understanding of emotions.
Our comprehensive experimentation showcases the substantial performance improvement obtained through the systematic incorporation of commonsense in ERC.
arXiv Detail & Related papers (2023-10-19T18:17:00Z) - DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations.
We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
arXiv Detail & Related papers (2023-06-15T10:41:23Z) - Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling
Approaches [28.47300996711215]
People rely heavily on context to enrich meaning beyond what is literally said.
To interact successfully with people, user-facing artificial intelligence systems will require similar skills in pragmatics.
arXiv Detail & Related papers (2022-11-15T18:21:46Z) - Few-shot Language Coordination by Modeling Theory of Mind [95.54446989205117]
We study the task of few-shot $textitlanguage coordination$.
We require the lead agent to coordinate with a $textitpopulation$ of agents with different linguistic abilities.
This requires the ability to model the partner's beliefs, a vital component of human communication.
arXiv Detail & Related papers (2021-07-12T19:26:11Z) - Facilitating the Communication of Politeness through Fine-Grained
Paraphrasing [18.471262688125645]
We take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance.
As a case study, we focus on facilitating the accurate transmission of pragmatic intentions.
We introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance.
arXiv Detail & Related papers (2020-11-30T19:00:00Z) - You Impress Me: Dialogue Generation via Mutual Persona Perception [62.89449096369027]
The research in cognitive science suggests that understanding is an essential signal for a high-quality chit-chat conversation.
Motivated by this, we propose P2 Bot, a transmitter-receiver based framework with the aim of explicitly modeling understanding.
arXiv Detail & Related papers (2020-04-11T12:51:07Z) - Emergence of Pragmatics from Referential Game between Theory of Mind
Agents [64.25696237463397]
We propose an algorithm, using which agents can spontaneously learn the ability to "read between lines" without any explicit hand-designed rules.
We integrate the theory of mind (ToM) in a cooperative multi-agent pedagogical situation and propose an adaptive reinforcement learning (RL) algorithm to develop a communication protocol.
arXiv Detail & Related papers (2020-01-21T19:37:33Z)
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.