# Target Guided Emotion Aware Chat Machine

arXiv: https://arxiv.org/abs/2011.07432 · HTML: https://mlsanity.com/paper/2011.07432/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Improving Personality Consistency in Conversation by Persona Extending](https://mlsanity.com/paper/2208.10816/) (2022)
- [Multi-Task Learning of Generation and Classification for Emotion-Aware Dialogue Response Generation](https://mlsanity.com/paper/2105.11696/) (2021)
- [Automatic Noisy Label Correction for Fine-Grained Entity Typing](https://mlsanity.com/paper/2205.03011/) (2022)
- [BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2204.03117/) (2022)
- [STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2211.15003/) (2022)
- [Incorporating Causal Analysis into Diversified and Logical Response Generation](https://mlsanity.com/paper/2209.09482/) (2022)
- [Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue](https://mlsanity.com/paper/2210.08801/) (2022)
- [A Unified Approach to Emotion Detection and Task-Oriented Dialogue Modeling](https://mlsanity.com/paper/2401.13789/) (2024)
- [Position Debiasing Fine-Tuning for Causal Perception in Long-Term Dialogue](https://mlsanity.com/paper/2406.02002/) (2024)
- [Multi-aspect Depression Severity Assessment via Inductive Dialogue System](https://mlsanity.com/paper/2410.21836/) (2024)

## Abstract (arXiv)

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem by proposing a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leverage target information for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.