# Augmenting Neural Response Generation with Context-Aware Topical Attention

arXiv: https://arxiv.org/abs/1811.01063 · HTML: https://mlsanity.com/paper/1811.01063/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2018.

## Built on by (most cited first, 27 of 27)

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- [Maria: A Visual Experience Powered Conversational Agent](https://mlsanity.com/paper/2105.13073/) (2021)
- [Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining](https://mlsanity.com/paper/2109.04080/) (2021)
- [A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2109.04096/) (2021)
- [GhostWriter: Augmenting Collaborative Human-AI Writing Experiences Through Personalization and Agency](https://mlsanity.com/paper/2402.08855/) (2024)
- [CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos](https://mlsanity.com/paper/2303.09713/) (2023)
- [ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems](https://mlsanity.com/paper/2305.07797/) (2023)
- [TikTalk: A Video-Based Dialogue Dataset for Multi-Modal Chitchat in Real World](https://mlsanity.com/paper/2301.05880/) (2023)
- [Multi-Party Conversational Agents: A Survey](https://mlsanity.com/paper/2505.18845/) (2025)
- [Towards Standard Criteria for human evaluation of Chatbots: A Survey](https://mlsanity.com/paper/2105.11197/) (2021)
- [PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits](https://mlsanity.com/paper/2401.07363/) (2024)
- [TopicRefine: Joint Topic Prediction and Dialogue Response Generation for Multi-turn End-to-End Dialogue System](https://mlsanity.com/paper/2109.05187/) (2021)
- [Text Style Transfer: An Introductory Overview](https://mlsanity.com/paper/2407.14822/) (2024)
- [Multi-turn Dialogue Comprehension from a Topic-aware Perspective](https://mlsanity.com/paper/2309.09666/) (2023)
- [Sequential Topic Selection Model with Latent Variable for Topic-Grounded Dialogue](https://mlsanity.com/paper/2210.08801/) (2022)
- [ZRIGF: An Innovative Multimodal Framework for Zero-Resource Image-Grounded Dialogue Generation](https://mlsanity.com/paper/2308.00400/) (2023)
- [Jointly modelling the evolution of social structure and language in online communities](https://mlsanity.com/paper/2409.19243/) (2024)
- [Inducing lexicons of in-group language with socio-temporal context](https://mlsanity.com/paper/2409.19257/) (2024)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [I run as fast as a rabbit, can you? A Multilingual Simile Dialogue Dataset](https://mlsanity.com/paper/2306.05672/) (2023)
- [Topical Language Generation using Transformers](https://mlsanity.com/paper/2103.06434/) (2021)
- [TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy](https://mlsanity.com/paper/2402.01714/) (2024)

## Abstract (arXiv)

Sequence-to-Sequence (Seq2Seq) models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically well-formed responses generated by these neural network models, they are prone to be acontextual, short and generic. In this work, we introduce a Topical Hierarchical Recurrent Encoder Decoder (THRED), a novel, fully data-driven, multi-turn response generation system intended to produce contextual and topic-aware responses. Our model is built upon the basic Seq2Seq model by augmenting it with a hierarchical joint attention mechanism that incorporates topical concepts and previous interactions into the response generation. To train our model, we provide a clean and high-quality conversational dataset mined from Reddit comments. We evaluate THRED on two novel automated metrics, dubbed Semantic Similarity and Response Echo Index, as well as with human evaluation. Our experiments demonstrate that the proposed model is able to generate more diverse and contextually relevant responses compared to the strong baselines.