Search Results for author: Xilun Chen

Found 24 papers, 15 papers with code

Efficient Open Domain Multi-Hop Question Answering with Few-Shot Data Synthesis

no code implementations23 May 2023 Mingda Chen, Xilun Chen, Wen-tau Yih

Few-shot learning for open domain multi-hop question answering typically relies on large language models (LLMs).

Fact Verification Few-Shot Learning +2

VideoOFA: Two-Stage Pre-Training for Video-to-Text Generation

no code implementations4 May 2023 Xilun Chen, Lili Yu, Wenhan Xiong, Barlas Oğuz, Yashar Mehdad, Wen-tau Yih

We propose a new two-stage pre-training framework for video-to-text generation tasks such as video captioning and video question answering: A generative encoder-decoder model is first jointly pre-trained on massive image-text data to learn fundamental vision-language concepts, and then adapted to video data in an intermediate video-text pre-training stage to learn video-specific skills such as spatio-temporal reasoning.

Question Answering Text Generation +3

Hierarchical Video-Moment Retrieval and Step-Captioning

1 code implementation CVPR 2023 Abhay Zala, Jaemin Cho, Satwik Kottur, Xilun Chen, Barlas Oğuz, Yasher Mehdad, Mohit Bansal

Our hierarchical benchmark consists of video retrieval, moment retrieval, and two novel moment segmentation and step captioning tasks.

Information Retrieval Moment Retrieval +4

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

Nonparametric Masked Language Modeling

1 code implementation2 Dec 2022 Sewon Min, Weijia Shi, Mike Lewis, Xilun Chen, Wen-tau Yih, Hannaneh Hajishirzi, Luke Zettlemoyer

Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases.

Language Modelling Masked Language Modeling +2

CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval

1 code implementation18 Nov 2022 Minghan Li, Sheng-Chieh Lin, Barlas Oguz, Asish Ghoshal, Jimmy Lin, Yashar Mehdad, Wen-tau Yih, Xilun Chen

In this paper, we unify different multi-vector retrieval models from a token routing viewpoint and propose conditional token interaction via dynamic lexical routing, namely CITADEL, for efficient and effective multi-vector retrieval.


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.


A Study on the Efficiency and Generalization of Light Hybrid Retrievers

no code implementations4 Oct 2022 Man Luo, Shashank Jain, Anchit Gupta, Arash Einolghozati, Barlas Oguz, Debojeet Chatterjee, Xilun Chen, Chitta Baral, Peyman Heidari

Driven by this question, we leverage an indexing-efficient dense retriever (i. e. DrBoost) and introduce a LITE retriever that further reduces the memory of DrBoost.

Adversarial Attack Contrastive Learning +1

Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?

2 code implementations13 Oct 2021 Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, Wen-tau Yih

Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse methods such as BM25 in their ability to reliably match salient phrases and rare entities in the query and to generalize to out-of-domain data.

Open-Domain Question Answering Passage Retrieval +1

Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing

no code implementations EMNLP 2020 Xilun Chen, Asish Ghoshal, Yashar Mehdad, Luke Zettlemoyer, Sonal Gupta

Task-oriented semantic parsing is a critical component of virtual assistants, which is responsible for understanding the user's intents (set reminder, play music, etc.).

Domain Adaptation Meta-Learning +2

Multi-Source Cross-Lingual Model Transfer: Learning What to Share

1 code implementation ACL 2019 Xilun Chen, Ahmed Hassan Awadallah, Hany Hassan, Wei Wang, Claire Cardie

In this work, we focus on the multilingual transfer setting where training data in multiple source languages is leveraged to further boost target language performance.

Cross-Lingual NER text-classification +2

Zero-Resource Multilingual Model Transfer: Learning What to Share

no code implementations27 Sep 2018 Xilun Chen, Ahmed Hassan Awadallah, Hany Hassan, Wei Wang, Claire Cardie

In this work, we propose a zero-resource multilingual transfer learning model that can utilize training data in multiple source languages, while not requiring target language training data nor cross-lingual supervision.

Cross-Lingual Transfer text-classification +2

Unsupervised Multilingual Word Embeddings

3 code implementations EMNLP 2018 Xilun Chen, Claire Cardie

Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space.

Multilingual Word Embeddings Translation +2

Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

2 code implementations TACL 2018 Xilun Chen, Yu Sun, Ben Athiwaratkun, Claire Cardie, Kilian Weinberger

To tackle the sentiment classification problem in low-resource languages without adequate annotated data, we propose an Adversarial Deep Averaging Network (ADAN) to transfer the knowledge learned from labeled data on a resource-rich source language to low-resource languages where only unlabeled data exists.

Classification Cross-Lingual Document Classification +5

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