Conversational Contextual Cues: The Case of Personalization and History for Response Ranking

By Rami Al-Rfou, Marc Pickett, Javier Snaider, Yun-Hsuan Sung, Brian Strope, R. Kurzweil. 2016

93 citations recorded by the source corpus.

We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different elements of the conversation. Unlike previous efforts, which focused on modeling messages and responses, we extend the modeling to long context and participant's history. Our system does not rely on handwritten rules or engineered features; instead, we train deep neural networks on a large conversational dataset. In particular, we exploit the structure of Reddit comments and posts to extract 2.1 billion messages and 133 million conversations. We evaluate our models on the task of predicting the next response in a conversation, and we find that modeling both context and participants improves prediction accuracy.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Federated Learning: Strategies for Improving Communication Efficiency (2016, methodology): “WORD PREDICTION ON REDDIT DATA We constructed the dataset for simulating Federated Learning based on the data containing publicly available posts/comments on Reddit (Google BigQuery), as described by Al-Rfou et al. (2016). Critically for our purposes, each post in the database is keyed by an author,”
Efficient Natural Language Response Suggestion for Smart Reply (2017, methodology): “This hierarchical structure results in models that learn how to use each feature faster than a network that sees all the features at once, and also allows for learning deeper networks than is otherwise possible [2].”
Neural Approaches to Conversational AI (2018, neutral): “rvative solution to mitigating blandness is to replace generation-based models with retrieval-based models for response generation (Jafarpour et al., 2010; Lu and Li, 2014; Inaba and Takahashi, 2016; Al-Rfou et al., 2016; Yan et al., 2016), in which the pool of possible responses is constructed in a”
Learning Semantic Textual Similarity from Conversations (2018, methodology): “The data for our experiments are drawn from Reddit conversations from 2007 to 2016, extracted by Al-Rfou et al. (2016).”
Federated Learning Of Out-Of-Vocabulary Words (2019, methodology): “Similar to work in (Al-Rfou et al., 2016), Reddit posts that have more than 1000 comments are excluded.”
Learning Differentially Private Language Models Without Losing Accuracy (2017, methodology): “Dataset We use a large public dataset of Reddit posts [21], as described by Al-Rfou et al. [3].”
DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset (2017, methodology): “Such approach is widely selected as baseline models in dialog generation Shang et al. (2015); Lowe et al. (2015); Al-Rfou’ et al. (2016).”
Multi-Mention Learning for Reading Comprehension with Neural Cascades (2017, methodology): “The idea of modeling separate classiﬁers that use complementary features comes from Al-Rfou et al. (2016) who found this gave considerable gains over combining all the features into a single model for a conversational system.”
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge (2022, methodology): “Prior works use Reddit data for conversational response selection [5, 139, 54] and abstractive summarization [127, 65] We release all post IDs and their corresponding metadata.”
A Repository of Conversational Datasets (2019, methodology): “…how often the correct response is identiﬁed as one of the top k ranked responses (Lowe et al., 2015; Inaba and Takahashi, 2016; Yu et al., 2016; Al-Rfou et al., 2016; Henderson et al., 2017; Lowe et al., 2017; Wu et al., 2017; Cer et al., 2018; Chaudhuri et al., 2018; Du and Black, 2018;…”
Efficient Intent Detection with Dual Sentence Encoders (2020, background): “A recent branch of sentence encoders moves beyond the standard LM-based pretraining objective, and proposes an alternative objective: conversational response selection, typically on Reddit data (Al-Rfou et al., 2016; Henderson et al., 2019a).”
Learning Differentially Private Recurrent Language Models (2017, methodology): “Dataset We use a large public dataset of Reddit posts, as described by Al-Rfou et al. (2016).”
Length bias in Encoder Decoder Models and a Case for Global Conditioning (2016, background): “It is also similar to work by (Al-Rfou et al., 2016), with the key difference being our focus on computing the normalized conditional probability, and our use of RNN encoders.”
ConveRT: Efficient and Accurate Conversational Representations from Transformers (2019, background): “we can relate target dialog tasks to general-domain conversational data such as Reddit (Al-Rfou et al., 2016).”