Attribution and Alignment: Effects of Local Context Repetition on
Utterance Production and Comprehension in Dialogue
- URL: http://arxiv.org/abs/2311.13061v1
- Date: Tue, 21 Nov 2023 23:50:33 GMT
- Title: Attribution and Alignment: Effects of Local Context Repetition on
Utterance Production and Comprehension in Dialogue
- Authors: Aron Molnar, Jaap Jumelet, Mario Giulianelli, Arabella Sinclair
- Abstract summary: Repetition is typically penalised when evaluating language model generations.
Humans use local and partner specific repetitions; these are preferred by human users and lead to more successful communication in dialogue.
In this study, we evaluate (a) whether language models produce human-like levels of repetition in dialogue, and (b) what are the processing mechanisms related to lexical re-use they use during comprehension.
- Score: 6.886248462185439
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Language models are often used as the backbone of modern dialogue systems.
These models are pre-trained on large amounts of written fluent language.
Repetition is typically penalised when evaluating language model generations.
However, it is a key component of dialogue. Humans use local and partner
specific repetitions; these are preferred by human users and lead to more
successful communication in dialogue. In this study, we evaluate (a) whether
language models produce human-like levels of repetition in dialogue, and (b)
what are the processing mechanisms related to lexical re-use they use during
comprehension. We believe that such joint analysis of model production and
comprehension behaviour can inform the development of cognitively inspired
dialogue generation systems.
Related papers
- PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue
Model [79.64376762489164]
PK-Chat is a Pointer network guided generative dialogue model, incorporating a unified pretrained language model and a pointer network over knowledge graphs.
The words generated by PK-Chat in the dialogue are derived from the prediction of word lists and the direct prediction of the external knowledge graph knowledge.
Based on the PK-Chat, a dialogue system is built for academic scenarios in the case of geosciences.
arXiv Detail & Related papers (2023-04-02T18:23:13Z) - Building a Personalized Dialogue System with Prompt-Tuning [5.942602139622984]
We build a dialogue system that responds based on a given character setting (persona)
We propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models.
arXiv Detail & Related papers (2022-06-11T02:21:11Z) - Back to the Future: Bidirectional Information Decoupling Network for
Multi-turn Dialogue Modeling [80.51094098799736]
We propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder.
BiDeN explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks.
Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
arXiv Detail & Related papers (2022-04-18T03:51:46Z) - Response Generation with Context-Aware Prompt Learning [19.340498579331555]
We present a novel approach for pre-trained dialogue modeling that casts the dialogue generation problem as a prompt-learning task.
Instead of fine-tuning on limited dialogue data, our approach, DialogPrompt, learns continuous prompt embeddings optimized for dialogue contexts.
Our approach significantly outperforms the fine-tuning baseline and the generic prompt-learning methods.
arXiv Detail & Related papers (2021-11-04T05:40:13Z) - A Review of Dialogue Systems: From Trained Monkeys to Stochastic Parrots [0.0]
We aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans.
We present a broad overview of methods developed to build dialogue systems over the years.
arXiv Detail & Related papers (2021-11-02T08:07:55Z) - "How Robust r u?": Evaluating Task-Oriented Dialogue Systems on Spoken
Conversations [87.95711406978157]
This work presents a new benchmark on spoken task-oriented conversations.
We study multi-domain dialogue state tracking and knowledge-grounded dialogue modeling.
Our data set enables speech-based benchmarking of task-oriented dialogue systems.
arXiv Detail & Related papers (2021-09-28T04:51:04Z) - Advances in Multi-turn Dialogue Comprehension: A Survey [51.215629336320305]
We review the previous methods from the perspective of dialogue modeling.
We discuss three typical patterns of dialogue modeling that are widely-used in dialogue comprehension tasks.
arXiv Detail & Related papers (2021-03-04T15:50:17Z) - Ranking Enhanced Dialogue Generation [77.8321855074999]
How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation.
Previous works usually employ various neural network architectures to model the history.
This paper proposes a Ranking Enhanced Dialogue generation framework.
arXiv Detail & Related papers (2020-08-13T01:49:56Z) - Neural Generation of Dialogue Response Timings [13.611050992168506]
We propose neural models that simulate the distributions of spoken response offsets.
The models are designed to be integrated into the pipeline of an incremental spoken dialogue system.
We show that human listeners consider certain response timings to be more natural based on the dialogue context.
arXiv Detail & Related papers (2020-05-18T23:00:57Z) - Knowledge Injection into Dialogue Generation via Language Models [85.65843021510521]
InjK is a two-stage approach to inject knowledge into a dialogue generation model.
First, we train a large-scale language model and query it as textual knowledge.
Second, we frame a dialogue generation model to sequentially generate textual knowledge and a corresponding response.
arXiv Detail & Related papers (2020-04-30T07:31:24Z)
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.