Extending Neural Generative Conversational Model using External Knowledge Sources

By Prasanna Parthasarathi, Joelle Pineau. 2018

70 citations recorded by the source corpus.

The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents trained with the Reddit News dataset, and consider incorporating external knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our experiments show faster training time and improved perplexity when leveraging external knowledge.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions (2020, background): “Nevertheless, a number of studies do report automatic metrics, sometimes without human studies (Lowe et al., 2015; Serban et al., 2016; Parthasarathi and Pineau, 2018).”
ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons (2019, background): “Nevertheless, a number of studies do report automatic metrics, without human studies (Serban et al., 2016; Parthasarathi and Pineau, 2018).”
Wizard of Wikipedia: Knowledge-Powered Conversational agents (2018, methodology): “Both Ghazvininejad et al. (2018) and Parthasarathi & Pineau (2018) use unstructured text instead, as we do: the former to discuss news articles using Wikipedia summaries as knowledge, and the latter to discuss local businesses in two-turn dialogues using Foursquare tips as knowledge.”
What makes a good conversation? How controllable attributes affect human judgments (2019, background): “Nevertheless, a number of studies only use automatic metrics, with no human study at all (Lowe et al., 2015; Parthasarathi and Pineau, 2018; Serban et al., 2016b).”
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (2019, background): “…conversation systems, such as open-domain dialogue systems (Han et al., 2015; Asghar et al., 2018; Ghazvinine-jad et al., 2018; Young et al., 2018; Parthasarathi and Pineau, 2018; Liu et al., 2018; Moghe et al., 2018; Dinan et al., 2019; Zhong et al., 2019), task-oriented dialogue systems…”
Sequential Latent Knowledge Selection for Knowledge-Grounded Dialogue (2020, background): “…conversational research (Zhang et al., 2018), a majority of previous works have studied how to effectively combine given knowledge and dialogue context to generate an utterance (Zhang et al., 2018; Li et al., 2019b; Parthasarathi & Pineau, 2018; Madotto et al., 2018; Gopalakrishnan et al., 2019).”
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs (2019, background): “Other line of work (Parthasarathi and Pineau, 2018; Ghazvininejad et al., 2018; Long et al., 2017) uses embedding vectors obtained from external knowledge sources (e.g. NELL (Carlson et al., 2010), Wikipedia, Freebase (Bast et al., 2014), free-form text, etc.) as an auxiliary input to the model in…”
Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks (2021, unknown): “Affective Commonsense Knowledge The significant role of external knowledge in many NLP tasks has been remarked on [32, 33, 34, 35, 36, 37].”
On-The-Fly Information Retrieval Augmentation for Language Models (2020, background): “, 2019), language generation (Parthasarathi and Pineau, 2018), knowledge base completion (Toutanova et al.”