Search Results for author: Alon Talmor

Found 14 papers, 8 papers with code

Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills

1 code implementation15 Jul 2021 Ori Yoran, Alon Talmor, Jonathan Berant

Models pre-trained with a language modeling objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning.

Language Modelling Reading Comprehension

Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge

1 code implementation NeurIPS 2020 Alon Talmor, Oyvind Tafjord, Peter Clark, Yoav Goldberg, Jonathan Berant

In this work, we provide a first demonstration that LMs can be trained to reliably perform systematic reasoning combining both implicit, pre-trained knowledge and explicit natural language statements.

oLMpics -- On what Language Model Pre-training Captures

1 code implementation31 Dec 2019 Alon Talmor, Yanai Elazar, Yoav Goldberg, Jonathan Berant

A fundamental challenge is to understand whether the performance of a LM on a task should be attributed to the pre-trained representations or to the process of fine-tuning on the task data.

Language Modelling

ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

no code implementations29 Dec 2019 Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Sameer Singh, Matt Gardner

A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context.

Entity Typing Machine Reading Comprehension +2

Comprehensive Multi-Dataset Evaluation of Reading Comprehension

no code implementations WS 2019 Dheeru Dua, Ananth Gottumukkala, Alon Talmor, Sameer Singh, Matt Gardner

A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context.

Entity Typing Natural Language Understanding +2

On Making Reading Comprehension More Comprehensive

no code implementations WS 2019 Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon Min

In this work, we justify a question answering approach to reading comprehension and describe the various kinds of questions one might use to more fully test a system{'}s comprehension of a passage, moving beyond questions that only probe local predicate-argument structures.

Machine Reading Comprehension Question Answering

MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension

1 code implementation WS 2019 Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen

We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems.

Multi-Task Learning Question Answering +1

Question Answering is a Format; When is it Useful?

no code implementations25 Sep 2019 Matt Gardner, Jonathan Berant, Hannaneh Hajishirzi, Alon Talmor, Sewon Min

In this opinion piece, we argue that question answering should be considered a format which is sometimes useful for studying particular phenomena, not a phenomenon or task in itself.

Machine Translation Question Answering +3

MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension

1 code implementation ACL 2019 Alon Talmor, Jonathan Berant

A large number of reading comprehension (RC) datasets has been created recently, but little analysis 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.

Reading Comprehension

CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

1 code implementation NAACL 2019 Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan Berant

To investigate question answering with prior knowledge, we present CommonsenseQA: a challenging new dataset for commonsense question answering.

Ranked #11 on Common Sense Reasoning on CommonsenseQA (using extra training data)

Common Sense Reasoning Question Answering

Repartitioning of the ComplexWebQuestions Dataset

no code implementations25 Jul 2018 Alon Talmor, Jonathan Berant

Recently, Talmor and Berant (2018) introduced ComplexWebQuestions - a dataset focused on answering complex questions by decomposing them into a sequence of simpler questions and extracting the answer from retrieved web snippets.

Reading Comprehension

The Web as a Knowledge-base for Answering Complex Questions

1 code implementation NAACL 2018 Alon Talmor, Jonathan Berant

In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model.

Reading Comprehension

Evaluating Semantic Parsing against a Simple Web-based Question Answering Model

1 code implementation SEMEVAL 2017 Alon Talmor, Mor Geva, Jonathan Berant

Semantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence.

Question Answering Semantic Parsing

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