Papers by Alon Talmor
oLMpics-On What Language Model Pre-training Captures (2020.tacl-1)
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| Challenge: | Recent success of pre-trained language models has spurred widespread interest in their capabilities. |
| Approach: | They propose an evaluation protocol that includes zero-shot evaluation and no fine-tuning . they propose to compare the learning curve of a fine- tuned LM to the learning of multiple controls . |
| Outcome: | The proposed evaluation protocol compares the learning curve of a fine-tuned LM to the learning of multiple controls. |
Comprehensive Multi-Dataset Evaluation of Reading Comprehension (D19-58)
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| Challenge: | Recent research aims to facilitate training and evaluation on several reading comprehension datasets at the same time. |
| Approach: | They propose an evaluation server that reports performance on seven diverse reading comprehension datasets and includes synthetic augmentations to test models' ability to handle out-of-domain questions. |
| Outcome: | The evaluation server performs on seven reading comprehension datasets, and collects and includes synthetic augmentations for these datasets to test models' ability to handle out-of-domain questions. |
MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension (D19-58)
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| Challenge: | MRQA datasets have been used to benchmark progress in general-purpose language understanding. |
| Approach: | They propose to combine 18 question answering datasets into one shared task to evaluate their generalization capabilities. |
| Outcome: | The best system achieved an average F1 score of 72.5 on the 12 held-out datasets, 10.7 absolute points higher than baseline based on BERT. |
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills (2022.acl-long)
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| Challenge: | Large pre-trained language models struggle in tasks that require reasoning . recent work shows that they struggle in performing symbolic reasoning operations without substantial amounts of additional data. |
| Approach: | They propose to leverage semi-structured tables and generate at scale question-paragraph pairs where answering the question requires reasoning over multiple facts in the paragraph. |
| Outcome: | The proposed model outperforms T5, a popular pre-trained encoder-decoder model, on reasoning-focused reading comprehension datasets. |
MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension (P19-1)
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| Challenge: | A large number of reading comprehension (RC) datasets have been created, but little research has been done on whether they generalize to one another and the extent to which existing datasets can be leveraged for improving performance on new ones. |
| Approach: | They propose a BERT-based reading comprehension model that can be trained on multiple RC datasets. |
| Outcome: | The proposed model can be trained on multiple RC datasets and improve performance on five RC data. |
On Making Reading Comprehension More Comprehensive (D19-58)
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| Challenge: | Getting machines to "understand" text is a vast and long-standing problem, made more challenging by the fact that it is not even clear what it means to understand text. |
| Approach: | They propose a question-based approach to machine reading comprehension that uses a natural language question to test a system's comprehension of a passage of text. |
| Outcome: | The proposed questions have surface cues or other biases that allow a model to shortcut the intended reasoning process. |
The Web as a Knowledge-Base for Answering Complex Questions (N18-1)
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| Challenge: | Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. |
| Approach: | They propose to decompose complex questions into a sequence of simple questions and compute the final answer from the sequence of answers. |
| Outcome: | The proposed framework improves performance from 20.8 precision@1 to 27.5 precision@1. |
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (N19-1)
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| Challenge: | Recent work on question answering relies on factoid questions with little general knowledge. |
| Approach: | They propose a dataset to capture commonsense question answering with prior knowledge . they extract multiple-choice questions that discriminate between the source and target concepts . |
| Outcome: | The proposed dataset captures commonsense reasoning beyond associations . it obtains 56% accuracy, well below human performance, which is 89% . |