# Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity

arXiv: https://arxiv.org/abs/1809.06873 · HTML: https://mlsanity.com/paper/1809.06873/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

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

- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)
- [Towards Facilitating Empathic Conversations in Online Mental Health Support: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.07714/) (2021)
- [DynaEval: Unifying Turn and Dialogue Level Evaluation](https://mlsanity.com/paper/2106.01112/) (2021)
- [Measuring Conversational Uptake: A Case Study on Student-Teacher Interactions](https://mlsanity.com/paper/2106.03873/) (2021)
- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Recent Trends in Personalized Dialogue Generation: A Review of Datasets, Methodologies, and Evaluations](https://mlsanity.com/paper/2405.17974/) (2024)
- [Generating Relevant and Coherent Dialogue Responses using Self-separated Conditional Variational AutoEncoders](https://mlsanity.com/paper/2106.03410/) (2021)
- [EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention](https://mlsanity.com/paper/2210.12463/) (2022)
- [Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization](https://mlsanity.com/paper/2109.06471/) (2021)
- [Learning to Select Context in a Hierarchical and Global Perspective for Open-domain Dialogue Generation](https://mlsanity.com/paper/2102.09282/) (2021)
- [MulZDG: Multilingual Code-Switching Framework for Zero-shot Dialogue Generation](https://mlsanity.com/paper/2208.08629/) (2022)
- [PVGRU: Generating Diverse and Relevant Dialogue Responses via Pseudo-Variational Mechanism](https://mlsanity.com/paper/2212.09086/) (2022)
- [Simulated Annealing for Emotional Dialogue Systems](https://mlsanity.com/paper/2109.10715/) (2021)
- [Balancing Multi-Domain Corpora Learning for Open-Domain Response Generation](https://mlsanity.com/paper/2205.02570/) (2022)
- [THINK: A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses](https://mlsanity.com/paper/2105.13630/) (2021)
- [WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialogue](https://mlsanity.com/paper/2108.01487/) (2021)
- [Stop Filtering: Multi-View Attribute-Enhanced Dialogue Learning](https://mlsanity.com/paper/2205.11206/) (2022)
- [SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation](https://mlsanity.com/paper/2208.08094/) (2022)
- [Affective Feedback Synthesis Towards Multimodal Text and Image Data](https://mlsanity.com/paper/2203.12692/) (2022)
- [Modeling Complex Dialogue Mappings via Sentence Semantic Segmentation Guided Conditional Variational Auto-Encoder](https://mlsanity.com/paper/2212.00231/) (2022)
- [Heterogeneous-Branch Collaborative Learning for Dialogue Generation](https://mlsanity.com/paper/2303.11621/) (2023)
- [Neural Conversation Models and How to Rein Them in: A Survey of Failures and Fixes](https://mlsanity.com/paper/2308.06095/) (2023)
- [ChatZero:Zero-shot Cross-Lingual Dialogue Generation via Pseudo-Target Language](https://mlsanity.com/paper/2408.08724/) (2024)
- [Relevance in Dialogue: Is Less More? An Empirical Comparison of Existing Metrics, and a Novel Simple Metric](https://mlsanity.com/paper/2206.01823/) (2022)
- [EXMODD: An EXplanatory Multimodal Open-Domain Dialogue dataset](https://mlsanity.com/paper/2310.10967/) (2023)
- [Synthesizing Sentiment-Controlled Feedback For Multimodal Text and Image Data](https://mlsanity.com/paper/2402.07640/) (2024)

## Abstract (arXiv)

We present three enhancements to existing encoder-decoder models for open-domain conversational agents, aimed at effectively modeling coherence and promoting output diversity: (1) We introduce a measure of coherence as the GloVe embedding similarity between the dialogue context and the generated response, (2) we filter our training corpora based on the measure of coherence to obtain topically coherent and lexically diverse context-response pairs, (3) we then train a response generator using a conditional variational autoencoder model that incorporates the measure of coherence as a latent variable and uses a context gate to guarantee topical consistency with the context and promote lexical diversity. Experiments on the OpenSubtitles corpus show a substantial improvement over competitive neural models in terms of BLEU score as well as metrics of coherence and diversity.