Search Results for author: Minjoon Seo

Found 30 papers, 21 papers with code

A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction

1 code implementation10 Jun 2022 Wonseok Hwang, Dongjun Lee, Kyoungyeon Cho, Hanuhl Lee, Minjoon Seo

Here we present the first large-scale benchmark of Korean legal AI datasets, LBox Open, that consists of one legal corpus, two classification tasks, two legal judgement prediction (LJP) tasks, and one summarization task.

Benchmark Language Modelling +1

Prompt Injection: Parameterization of Fixed Inputs

1 code implementation31 May 2022 Eunbi Choi, Yongrae Jo, Joel Jang, Minjoon Seo

Through these explorations, we show that PI can be a promising direction for conditioning language models, especially in scenarios with long and fixed prompts.

Semantic Parsing Zero-Shot Learning

Beyond Fact Verification: Comparing and Contrasting Claims on Contentious Topics

no code implementations24 May 2022 Miyoung Ko, Ingyu Seong, Hwaran Lee, Joonsuk Park, Minsuk Chang, Minjoon Seo

As the importance of identifying misinformation is increasing, many researchers focus on verifying textual claims on the web.

Fact Verification Misinformation

TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models

1 code implementation29 Apr 2022 Joel Jang, Seonghyeon Ye, Changho Lee, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Minjoon Seo

Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a phenomenon called temporal misalignment.

Benchmark Continual Learning

Generative Multi-hop Retrieval

no code implementations27 Apr 2022 Hyunji Lee, Sohee Yang, Hanseok Oh, Minjoon Seo

Multi-hop retrieval is the task of retrieving a series of multiple documents that together provide sufficient evidence to answer a natural language query.

Towards Continual Knowledge Learning of Language Models

2 code implementations ICLR 2022 Joel Jang, Seonghyeon Ye, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Stanley Jungkyu Choi, Minjoon Seo

By highlighting the critical causes of knowledge forgetting, we show that CKL is a challenging and important problem that helps us better understand and train ever-changing LMs.

Benchmark Continual Learning +2

Cost-effective End-to-end Information Extraction for Semi-structured Document Images

no code implementations EMNLP 2021 Wonseok Hwang, Hyunji Lee, Jinyeong Yim, Geewook Kim, Minjoon Seo

A real-world information extraction (IE) system for semi-structured document images often involves a long pipeline of multiple modules, whose complexity dramatically increases its development and maintenance cost.

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering

1 code implementation NAACL 2021 Sohee Yang, Minjoon Seo

In open-domain question answering (QA), retrieve-and-read mechanism has the inherent benefit of interpretability and the easiness of adding, removing, or editing knowledge compared to the parametric approaches of closed-book QA models.

Open-Domain Question Answering

Is Retriever Merely an Approximator of Reader?

no code implementations21 Oct 2020 Sohee Yang, Minjoon Seo

The state of the art in open-domain question answering (QA) relies on an efficient retriever that drastically reduces the search space for the expensive reader.

Open-Domain Question Answering

Spatial Dependency Parsing for Semi-Structured Document Information Extraction

1 code implementation Findings (ACL) 2021 Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Sohee Yang, Minjoon Seo

Information Extraction (IE) for semi-structured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories.

Dependency Parsing

Syntactic Question Abstraction and Retrieval for Data-Scarce Semantic Parsing

no code implementations AKBC 2020 Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Minjoon Seo

Deep learning approaches to semantic parsing require a large amount of labeled data, but annotating complex logical forms is costly.

Semantic Parsing

Contextualized Sparse Representations for Real-Time Open-Domain Question Answering

3 code implementations ACL 2020 Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi, Jaewoo Kang

Open-domain question answering can be formulated as a phrase retrieval problem, in which we can expect huge scalability and speed benefit but often suffer from low accuracy due to the limitation of existing phrase representation models.

Information Retrieval Open-Domain 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

Mixture Content Selection for Diverse Sequence Generation

1 code implementation IJCNLP 2019 Jaemin Cho, Minjoon Seo, Hannaneh Hajishirzi

The diversification stage uses a mixture of experts to sample different binary masks on the source sequence for diverse content selection.

Abstractive Text Summarization Document Summarization +1

Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index

1 code implementation ACL 2019 Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

Existing open-domain question answering (QA) models are not suitable for real-time usage because they need to process several long documents on-demand for every input query.

Open-Domain Question Answering

A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization

5 code implementations4 Feb 2019 Wonseok Hwang, Jinyeong Yim, Seunghyun Park, Minjoon Seo

We present SQLova, the first Natural-language-to-SQL (NL2SQL) model to achieve human performance in WikiSQL dataset.

Semantic Parsing

Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension

1 code implementation EMNLP 2018 Minjoon Seo, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi

We formalize a new modular variant of current question answering tasks by enforcing complete independence of the document encoder from the question encoder.

Question Answering Reading Comprehension

Neural Speed Reading via Skim-RNN

1 code implementation ICLR 2018 Minjoon Seo, Sewon Min, Ali Farhadi, Hannaneh Hajishirzi

Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens.

Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension

no code implementations CVPR 2017 Aniruddha Kembhavi, Minjoon Seo, Dustin Schwenk, Jonghyun Choi, Ali Farhadi, Hannaneh Hajishirzi

Our analysis shows that a significant portion of questions require complex parsing of the text and the diagrams and reasoning, indicating that our dataset is more complex compared to previous machine comprehension and visual question answering datasets.

Question Answering Reading Comprehension +1

Zero-Shot Relation Extraction via Reading Comprehension

2 code implementations CONLL 2017 Omer Levy, Minjoon Seo, Eunsol Choi, Luke Zettlemoyer

We show that relation extraction can be reduced to answering simple reading comprehension questions, by associating one or more natural-language questions with each relation slot.

Reading Comprehension Relation Extraction +2

Question Answering through Transfer Learning from Large Fine-grained Supervision Data

1 code implementation ACL 2017 Sewon Min, Minjoon Seo, Hannaneh Hajishirzi

We show that the task of question answering (QA) can significantly benefit from the transfer learning of models trained on a different large, fine-grained QA dataset.

Question Answering Transfer Learning

Bidirectional Attention Flow for Machine Comprehension

23 code implementations5 Nov 2016 Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, Hannaneh Hajishirzi

Machine comprehension (MC), answering a query about a given context paragraph, requires modeling complex interactions between the context and the query.

Cloze Test Open-Domain Question Answering +1

A Diagram Is Worth A Dozen Images

1 code implementation24 Mar 2016 Aniruddha Kembhavi, Mike Salvato, Eric Kolve, Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi

We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering.

Computer Vision Visual Question Answering

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