# Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations

arXiv: https://arxiv.org/abs/2106.09896 · HTML: https://mlsanity.com/paper/2106.09896/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 18 Jun 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension](https://mlsanity.com/paper/1902.00164/) (2019)
- [Discovering Discrete Latent Topics with Neural Variational Inference](https://mlsanity.com/paper/1706.00359/) (2017)
- [ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://mlsanity.com/paper/1906.01265/) (2019)
- [Topic-Aware Neural Keyphrase Generation for Social Media Language](https://mlsanity.com/paper/1906.03889/) (2019)
- [GSN: A Graph-Structured Network for Multi-Party Dialogues](https://mlsanity.com/paper/1905.13637/) (2019)
- [Topic Memory Networks for Short Text Classification](https://mlsanity.com/paper/1809.03664/) (2018)
- [Microblog Hashtag Generation via Encoding Conversation Contexts](https://mlsanity.com/paper/1905.07584/) (2019)
- [What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations](https://mlsanity.com/paper/1903.07319/) (2019)

## Built on by (most cited first, 8 of 8)

- [IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators](https://mlsanity.com/paper/2402.00345/) (2024)
- [ePiC: Employing Proverbs in Context as a Benchmark for Abstract Language Understanding](https://mlsanity.com/paper/2109.06838/) (2021)
- [QuoteR: A Benchmark of Quote Recommendation for Writing](https://mlsanity.com/paper/2202.13145/) (2022)
- [When Meaning Isn't Literal: Exploring Idiomatic Meaning Across Languages and Modalities](https://mlsanity.com/paper/2604.10787/) (2026)
- [Re-entry Prediction for Online Conversations via Self-Supervised Learning](https://mlsanity.com/paper/2109.02020/) (2021)
- [Efficient Pointwise-Pairwise Learning-to-Rank for News Recommendation](https://mlsanity.com/paper/2409.17711/) (2024)
- [Topic-Guided Self-Introduction Generation for Social Media Users](https://mlsanity.com/paper/2305.15138/) (2023)
- [QUILL: Quotation Generation Enhancement of Large Language Models](https://mlsanity.com/paper/2411.03675/) (2024)

## Abstract (arXiv)

Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for both humans and machines. This work studies automatic quotation generation in an online conversation and explores how language consistency affects whether a quotation fits the given context. Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn's existing content. Further, an encoder-decoder neural framework is employed to continue the context with a quotation via language generation. Experiment results on two large-scale datasets in English and Chinese demonstrate that our quotation generation model outperforms the state-of-the-art models. Further analysis shows that topic, interaction, and query consistency are all helpful to learn how to quote in online conversations.