Papers by Harm de Vries
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching (2022.tacl-1)
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| Challenge: | Current datasets for conversational question answering do not contain topic switches . people often engage in information-seeking conversations to discover new knowledge . |
| Approach: | They propose an open-domain conversational dataset with topic switches based on Wikipedia. |
| Outcome: | The proposed dataset achieves an F1 of 55.8, falling short of human performance by 14.2 points, indicating the difficulty of the dataset. |
The Power of Prompt Tuning for Low-Resource Semantic Parsing (2022.acl-short)
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| Challenge: | Prompt tuning is an effective method for adapting pre-trained language models to downstream tasks. |
| Approach: | They propose to use prompt tuning for semantic parsing to map natural language utterances onto formal meaning representations. |
| Outcome: | The proposed method outperforms the fine-tuned model on low-resource splits of Overnight and TOPv2 on language representations with increasing model scale and target representations. |
DuoRAT: Towards Simpler Text-to-SQL Models (2021.naacl-main)
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| Challenge: | Recent text-to-SQL models can translate natural language questions to corresponding SQL queries on unseen databases. |
| Approach: | They propose a re-implementation of the RAT-SQL model that uses only relation-aware or vanilla transformers as the building blocks. |
| Outcome: | The proposed model is based on the spider dataset and shows it can be used on large databases without human intervention. |
The StatCan Dialogue Dataset: Retrieving Data Tables through Conversations with Genuine Intents (2023.eacl-main)
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| Challenge: | StatCan Dialogue Dataset consists of 19,379 conversation turns between agents and online users . researchers propose two tasks to help knowledge workers find relevant tables for live chat users based on real-world intents . |
| Approach: | They propose two tasks based on 19,379 conversation turns between agents and online users . they investigate the difficulty of each task by establishing strong baselines . |
| Outcome: | The proposed task is based on a dataset of 19,379 conversation turns . the researchers show that the models struggle to generalize to future conversations . |