Papers by Marilyn Walker
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. |
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG (P19-1)
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| Challenge: | Neural natural language generation (NNLG) models generate syntactically correct utterances from structured inputs without needing hand-crafted rules or templates. |
| Approach: | They propose a method for generating a corpus of parallel meaning representations with rich style markup using freely available and naturally descriptive user reviews. |
| Outcome: | The proposed method can be scalably reused to generate NLG datasets for other domains. |
OpenEL: An Annotated Corpus for Entity Linking and Discourse in Open Domain Dialogue (2022.lrec-1)
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| Challenge: | Named entity recognition (NER), named entity linking and discourse modeling are crucial aspects of natural language understanding for open domain dialogue systems. |
| Approach: | They present an annotated multi-domain corpus for linking entities in open-domain dialogue . they use dialogue context and anaphora resolution to assess the effectiveness of the task . |
| Outcome: | The OpenEL corpus is an annotated multi-domain corpus for linking entities in open-domain dialogue . the system Flair + BLINK has the best performance with a 0.65 F1 score . |
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. |
Exploring Conversational Language Generation for Rich Content about Hotels (L18-1)
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| Challenge: | a new method is needed to generate natural dialogues for hotel information . a recent study shows that hotel descriptions are not a good match for conversational interaction . |
| Approach: | They propose to use stylistic features to generate and score hotel dialogues from hotel descriptions . they use hotel descriptions written by human writers within Google Content Studio . |
| Outcome: | The proposed models can be used to generate natural dialogues for hotels . the authors show that the sentences in the original written hotel descriptions are not a good match for conversational interaction. |
Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation (2020.acl-main)
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| Challenge: | Current sequence-to-sequence models require serialized input, resulting in loss of structural information. |
| Approach: | They propose a dual encoding model that incorporates the graph structure and caters to the linear structure of the output text. |
| Outcome: | Empirical results show that dual encoding can improve the quality of natural language descriptions. |
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. |