Search Results for author: Akari Asai

Found 26 papers, 17 papers with code

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

1 code implementation17 Oct 2023 Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, Hannaneh Hajishirzi

Our framework trains a single arbitrary LM that adaptively retrieves passages on-demand, and generates and reflects on retrieved passages and its own generations using special tokens, called reflection tokens.

Fact Verification Response Generation +1

BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer

no code implementations24 May 2023 Akari Asai, Sneha Kudugunta, Xinyan Velocity Yu, Terra Blevins, Hila Gonen, Machel Reid, Yulia Tsvetkov, Sebastian Ruder, Hannaneh Hajishirzi

Despite remarkable advancements in few-shot generalization in natural language processing, most models are developed and evaluated primarily in English.

Benchmarking Cross-Lingual Transfer

TaskWeb: Selecting Better Source Tasks for Multi-task NLP

1 code implementation22 May 2023 Joongwon Kim, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi

TaskShop uses TaskWeb to estimate the benefit of using a source task for learning a new target task, and to choose a subset of helpful training tasks for multi-task training.

Multi-Task Learning

xPQA: Cross-Lingual Product Question Answering across 12 Languages

1 code implementation16 May 2023 Xiaoyu Shen, Akari Asai, Bill Byrne, Adrià De Gispert

To study this practical industrial task, we present xPQA, a large-scale annotated cross-lingual PQA dataset in 12 languages across 9 branches, and report results in (1) candidate ranking, to select the best English candidate containing the information to answer a non-English question; and (2) answer generation, to generate a natural-sounding non-English answer based on the selected English candidate.

Answer Generation Machine Translation +3

How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

1 code implementation15 Feb 2023 Sheng-Chieh Lin, Akari Asai, Minghan Li, Barlas Oguz, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen

We hence propose a new DA approach with diverse queries and sources of supervision to progressively train a generalizable DR. As a result, DRAGON, our dense retriever trained with diverse augmentation, is the first BERT-base-sized DR to achieve state-of-the-art effectiveness in both supervised and zero-shot evaluations and even competes with models using more complex late interaction (ColBERTv2 and SPLADE++).

Contrastive Learning Data Augmentation +1

When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

1 code implementation20 Dec 2022 Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi

Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge.

Knowledge Probing Memorization +2

Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources

no code implementations28 Nov 2022 Xinyan Velocity Yu, Akari Asai, Trina Chatterjee, Junjie Hu, Eunsol Choi

While the NLP community is generally aware of resource disparities among languages, we lack research that quantifies the extent and types of such disparity.

Task-aware Retrieval with Instructions

1 code implementation16 Nov 2022 Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, Wen-tau Yih

We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries.


MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages

no code implementations NAACL (MIA) 2022 Akari Asai, Shayne Longpre, Jungo Kasai, Chia-Hsuan Lee, Rui Zhang, Junjie Hu, Ikuya Yamada, Jonathan H. Clark, Eunsol Choi

We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages.

Question Answering Retrieval

ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts

1 code implementation24 May 2022 Akari Asai, Mohammadreza Salehi, Matthew E. Peters, Hannaneh Hajishirzi

Our method, called ATTEMPT (ATTEntional Mixtures of Prompt Tuning), obtains source prompts as encodings of large-scale source tasks into a small number of parameters and trains an attention module to interpolate the source prompts and a newly initialized target prompt for every instance in the target task.

Few-Shot Learning Language Modelling +1

Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks

1 code implementation NAACL 2022 Akari Asai, Matt Gardner, Hannaneh Hajishirzi

We introduce a multi-task learning framework to jointly generate the final output and predict the evidentiality of each passage, leveraging a new task-agnostic method to obtain silver evidentiality labels for supervision.

Fact Verification Memorization +3

One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval

1 code implementation NeurIPS 2021 Akari Asai, Xinyan Yu, Jungo Kasai, Hannaneh Hajishirzi

We present Cross-lingual Open-Retrieval Answer Generation (CORA), the first unified many-to-many question answering (QA) model that can answer questions across many languages, even for ones without language-specific annotated data or knowledge sources.

Answer Generation Passage Retrieval +3

Efficient Passage Retrieval with Hashing for Open-domain Question Answering

1 code implementation ACL 2021 Ikuya Yamada, Akari Asai, Hannaneh Hajishirzi

Most state-of-the-art open-domain question answering systems use a neural retrieval model to encode passages into continuous vectors and extract them from a knowledge source.

Natural Questions Open-Domain Question Answering +3

Challenges in Information-Seeking QA: Unanswerable Questions and Paragraph Retrieval

no code implementations ACL 2021 Akari Asai, Eunsol Choi

However, datasets containing information-seeking queries where evidence documents are provided after the queries are written independently remain challenging.

Language Modelling Natural Questions +3

Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT

no code implementations27 Feb 2020 Lichao Sun, Kazuma Hashimoto, Wenpeng Yin, Akari Asai, Jia Li, Philip Yu, Caiming Xiong

There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously.

Question Answering Sentiment Analysis

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

2 code implementations ICLR 2020 Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, Caiming Xiong

Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question.

Question Answering Retrieval

Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia

no code implementations EMNLP 2020 Ikuya Yamada, Akari Asai, Jin Sakuma, Hiroyuki Shindo, Hideaki Takeda, Yoshiyasu Takefuji, Yuji Matsumoto

The embeddings of entities in a large knowledge base (e. g., Wikipedia) are highly beneficial for solving various natural language tasks that involve real world knowledge.

World Knowledge

Multilingual Extractive Reading Comprehension by Runtime Machine Translation

1 code implementation10 Sep 2018 Akari Asai, Akiko Eriguchi, Kazuma Hashimoto, Yoshimasa Tsuruoka

Given a target language without RC training data and a pivot language with RC training data (e. g. English), our method leverages existing RC resources in the pivot language by combining a competitive RC model in the pivot language with an attentive Neural Machine Translation (NMT) model.

Machine Translation NMT +2

HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments

2 code implementations LREC 2018 Akari Asai, Sara Evensen, Behzad Golshan, Alon Halevy, Vivian Li, Andrei Lopatenko, Daniela Stepanov, Yoshihiko Suhara, Wang-Chiew Tan, Yinzhan Xu

The science of happiness is an area of positive psychology concerned with understanding what behaviors make people happy in a sustainable fashion.

Art Analysis

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