Papers by Victor Zhong
Multi-hop Reading Comprehension through Question Decomposition and Rescoring (P19-1)
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| Challenge: | Existing systems for multi-hop reading comprehension decompose compositional questions into simpler sub-questions . authors propose a system that learns to break compositional multi- hop questions into simple singlehop sub-question . |
| Approach: | They propose a system that decomposes a compositional question into simpler sub-questions . they propose recast subquestion generation as a span prediction problem . |
| Outcome: | The proposed system generates as effective as human-authored sub-questions using 400 examples . it also provides explainable evidence for its decision making in the form of sub-questions . |
Efficient and Robust Question Answering from Minimal Context over Documents (P18-1)
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| Challenge: | Recent work shows that neural QA models are sensitive to adversarial inputs. |
| Approach: | They propose a sentence selector to select the minimal set of sentences to feed into a QA model. |
| Outcome: | The proposed system reduces training time and inference time by up to 13 times . it is comparable to or better than the state-of-the-art on SQuAD, NewsQA, TriviaQA and SQu AD-Open . |
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories (2023.acl-long)
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| Challenge: | Large language models struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters. |
| Approach: | They propose a retrieval-augmentation method that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary. |
| Outcome: | The proposed method improves performance and reduces inference costs by only retrieving non-parametric memories when necessary. |
LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer (2021.findings-acl)
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| Challenge: | Recent work on text style transfer proposes single-span editing as an alternative to generating the target text from scratch. |
| Approach: | They propose a coarse-to-fine editor for style transfer that transforms text using Levenshtein edit operations (e.g. insert, replace, delete). |
| Outcome: | The proposed method outperforms existing methods on sentiment and politeness transfer and improves model performance. |
UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (2022.emnlp-main)
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Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, Tao Yu
| Challenge: | Structured knowledge grounding (SKG) uses structured knowledge to complete user requests . since inputs and outputs of SKG tasks are heterogeneous, they have been studied separately . |
| Approach: | They propose a framework that unifies 21 SKG tasks into a text-to-text format . they use unifiedSKG to benchmark T5 with different sizes . |
| Outcome: | The proposed framework unifies 21 SKG tasks into a text-to-text format . it achieves state-of-the-art performance on almost all of the 21 tasks, the authors show . |
Grounded Adaptation for Zero-shot Executable Semantic Parsing (2020.emnlp-main)
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| Challenge: | Existing semantic parsers are usually engineered for each application environment, but they struggle when deployed to a new database. |
| Approach: | They propose a method to adapt existing semantic parsers to new environments . they propose combining a forward semantic parsed with a backward utterance generator to synthesize data in the new environment and select cycle-consistent examples to adapt the parser. |
| Outcome: | The proposed procedure outperforms data-augmentation and improves execution accuracy on the Spider, Sparc, and CoSQL zero-shot semantic parsing tasks. |
E3: Entailment-driven Extracting and Editing for Conversational Machine Reading (P19-1)
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| Challenge: | Conversational machine reading systems help users answer high-level questions when they do not know the exact rules by which the decision is made. |
| Approach: | They propose a conversational machine reading model that extracts a set of decision rules from a procedural text which the system must read to figure out what to ask the user. |
| Outcome: | The proposed model outperforms existing systems and a BERT-based baseline on the ShARC conversational machine reading dataset and provides an explainable alternative to prior work. |
RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering (2023.findings-emnlp)
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| Challenge: | Existing QA models are not robust to variations in question constraints, but can be made more robust by tuning on clusters of related questions. |
| Approach: | They introduce RoMQA, the first benchmark for robust, multi-evidence, multianswer question answering (QA) RoMQ contains clusters of related questions that are derived from the Wikidata knowledge graph . |
| Outcome: | The proposed model is the first benchmark for robust, multi-evidence, multianswer question answering (QA) compared to prior QA datasets, it has more human-written questions that require reasoning over more evidence text and have, on average, many more correct answers. |
Global-Locally Self-Attentive Encoder for Dialogue State Tracking (P18-1)
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| Challenge: | a global-local self-attentive dialogue state tracker estimates user goals and requests given the dialogue context . GLAD significantly improves tracking of rare states, compared to prior work . task-oriented dialogue systems can significantly reduce operating costs . |
| Approach: | They propose a global-local self-attentive dialogue state tracker which shares global-level modules with global-specific estimators for different types of dialogue states. |
| Outcome: | The proposed model outperforms previous models on the WoZ state tracking task by 3.9% and 4.8%. |
M2D2: A Massively Multi-Domain Language Modeling Dataset (2022.emnlp-main)
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| Challenge: | M2D2 consists of 8.5B tokens and spans 145 domains extracted from Wikipedia and Semantic Scholar. |
| Approach: | They propose to organize 145 domains into 22 groups and use ontologies from Wikipedia and ArXiv to study domain adaptation in language models. |
| Outcome: | The proposed model enables the study of domain adaptation in language models (LMs) it shows that small amounts of fine-grained data can lead to larger in-domain performance gains than weakly relevant data. |