Papers by Kevin Bowden
Athena 2.0: Contextualized Dialogue Management for an Alexa Prize SocialBot (2021.emnlp-demo)
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Juraj Juraska, Kevin Bowden, Lena Reed, Vrindavan Harrison, Wen Cui, Omkar Patil, Rishi Rajasekaran, Angela Ramirez, Cecilia Li, Eduardo Zamora, Phillip Lee, Jeshwanth Bheemanpally, Rohan Pandey, Adwait Ratnaparkhi, Marilyn Walker
| Challenge: | Athena 2.0 is a socialbot that has been a finalist in the last two Alexa Prize Grand Challenges. |
| Approach: | They describe Athena 2.0's dialogue management strategy and its performance in the Alexa Prize 20/21 competition. |
| Outcome: | The system is a finalist in the Alexa Prize 20/21 competition and will be shown on a live demo and recorded video recordings. |
SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems (L18-1)
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| Challenge: | UCSC researchers have developed an open domain social bot aimed at casual conversation . NER and NEL are important preprocessing steps for understanding user intent in open domain dialogue systems. |
| Approach: | They propose a tool for NER and NEL in open domain dialogue that addresses these challenges . they also propose two corpora based on 10,000 real user conversations . |
| Outcome: | The proposed open domain social bot is aimed at casual conversation. |
A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation (N18-1)
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| Challenge: | a recent study has shown that natural language generators produce utterances with humanlike coherence and naturalness for many different kinds of content. |
| Approach: | They propose to use a neural language generator to generate a syntactically and semantically correct utterance from a given MR. |
| Outcome: | The proposed model outperforms state-of-the-art models on restaurant, TV and laptop datasets. |
Active Listening: Personalized Question Generation in Open-Domain Social Conversation with User Model Based Prompting (2024.findings-emnlp)
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| Challenge: | Existing work shows that users of conversational systems want a more personalized experience . Question Generation tasks focus on factual questions from textual excerpts . |
| Approach: | They hypothesize that conversational systems want a more personalized experience . they use large language models capable of casual conversation to generate PQs . |
| Outcome: | The proposed model produces the most natural and engaging responses against competing models. |
Implicit Discourse Relation Identification for Open-domain Dialogues (P19-1)
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| Challenge: | Discourse relation identification is a challenging problem in open-domain dialogue systems . previous work relies on formal text but this data is not suitable for informal dialogue . |
| Approach: | They propose a method to automatically extract the implicit discourse relation argument pairs from dialogic turns and a pipeline to identify them. |
| Outcome: | The proposed pipeline extracts argument pairs from dialogic turns and improves it by performing feature ablation and incorporating dialogue features. |