Papers by Marcus Collins
VoiSeR: A New Benchmark for Voice-Based Search Refinement (2021.eacl-main)
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| Challenge: | a new study shows that voice-based search systems are challenging to support in the context of the user intent of voice searches . support for voice-driven search, exploration, and refinement is a fundamental aspect of voice assistants . |
| Approach: | They propose to use crowdsourcing to collect voice-based search refinements . they use 10,000 search refinement utterances to annotate a search intent . |
| Outcome: | The proposed dataset shows that voice-based search refinements can support most common tasks . the study shows that the proposed dataset can support research in conversational query understanding . |
Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
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| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
| Approach: | They evaluate the effectiveness of cycle training by using two models which are inverses of each other to generate text from structured data and one which generates the structured data from natural language text. |
| Outcome: | The proposed approach achieves nearly the same performance as fully supervised approaches on the WebNLG, E2E, WTQ, and WSQL datasets. |
Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings (2022.coling-1)
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Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko, Eugene Agichtein
| Challenge: | Existing Conversational Task Assistants fail to provide a comprehensive natural conversation that includes search, context-aware QA, step-by-step instructions. |
| Approach: | They present a corpus of conversations in two domains: cooking and home improvement . they crowd-sourced 549 conversations with an asynchronous Wizard-of-Oz setup . |
| Outcome: | The proposed model performs well in both Intent Classification and Abstractive Question Answering tasks, but the performance is poor on AQA tasks. |
Fact Checking Machine Generated Text with Dependency Trees (2022.emnlp-industry)
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| Challenge: | Recent work has noted the benefits of natural language text generated by NLG systems over fixed templates. |
| Approach: | They propose a method that checks factuality of input text based on structured knowledge patterns and dependency relations with respect to the input text. |
| Outcome: | The proposed technique outperforms state-of-the-art techniques in this special, but important case. |