# Lexical Entrainment for Conversational Systems

arXiv: https://arxiv.org/abs/2310.09651 · HTML: https://mlsanity.com/paper/2310.09651/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 34 of its 66 references that are in the map.
- First posted on arXiv 14 Oct 2023.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1905.08743/) (2019)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://mlsanity.com/paper/1909.05358/) (2019)
- [TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking](https://mlsanity.com/paper/2005.02877/) (2020)
- [Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1612.01627/) (2016)
- [MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2009.12005/) (2020)
- [Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/1704.00057/) (2017)
- [Efficient Dialogue State Tracking by Selectively Overwriting Memory](https://mlsanity.com/paper/1911.03906/) (2019)
- [StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts](https://mlsanity.com/paper/2204.08292/) (2022)
- [Shades of BLEU, Flavours of Success: The Case of MultiWOZ](https://mlsanity.com/paper/2106.05555/) (2021)
- [Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention](https://mlsanity.com/paper/1905.12866/) (2019)
- [DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning](https://mlsanity.com/paper/2309.05173/) (2023)
- [AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models](https://mlsanity.com/paper/2102.05126/) (2021)
- [Learning to Execute Actions or Ask Clarification Questions](https://mlsanity.com/paper/2204.08373/) (2022)
- [A Survey on Asking Clarification Questions Datasets in Conversational Systems](https://mlsanity.com/paper/2305.15933/) (2023)
- [Efficient Context and Schema Fusion Networks for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2004.03386/) (2020)
- [Multi-Domain Dialogue Acts and Response Co-Generation](https://mlsanity.com/paper/2004.12363/) (2020)
- [Extending CLIP for Category-to-image Retrieval in E-commerce](https://mlsanity.com/paper/2112.11294/) (2021)
- [Don't Stop Pretraining? Make Prompt-based Fine-tuning Powerful Learner](https://mlsanity.com/paper/2305.01711/) (2023)
- [Rethinking Semi-supervised Learning with Language Models](https://mlsanity.com/paper/2305.13002/) (2023)
- [A Context-aware Natural Language Generator for Dialogue Systems](https://mlsanity.com/paper/1608.07076/) (2016)
- [Experiments on Generalizability of User-Oriented Fairness in Recommender Systems](https://mlsanity.com/paper/2205.08289/) (2022)
- [Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation](https://mlsanity.com/paper/2001.08961/) (2020)
- [Modeling Interpersonal Linguistic Coordination in Conversations using Word Mover's Distance](https://mlsanity.com/paper/1904.06002/) (2019)

## Abstract (arXiv)

Conversational agents have become ubiquitous in assisting with daily tasks, and are expected to possess human-like features. One such feature is lexical entrainment (LE), a phenomenon in which speakers in human-human conversations tend to naturally and subconsciously align their lexical choices with those of their interlocutors, leading to more successful and engaging conversations. As an example, if a digital assistant replies 'Your appointment for Jinling Noodle Pub is at 7 pm' to the question 'When is my reservation for Jinling Noodle Bar today?', it may feel as though the assistant is trying to correct the speaker, whereas a response of 'Your reservation for Jinling Noodle Bar is at 7 pm' would likely be perceived as more positive. This highlights the importance of LE in establishing a shared terminology for maximum clarity and reducing ambiguity in conversations. However, we demonstrate in this work that current response generation models do not adequately address this crucial humanlike phenomenon. To address this, we propose a new dataset, named MULTIWOZ-ENTR, and a measure for LE for conversational systems. Additionally, we suggest a way to explicitly integrate LE into conversational systems with two new tasks, a LE extraction task and a LE generation task. We also present two baseline approaches for the LE extraction task, which aim to detect LE expressions from dialogue contexts.