Search Results for author: Yinfei Yang

Found 47 papers, 18 papers with code

MultiReQA: A Cross-Domain Evaluation forRetrieval Question Answering Models

1 code implementation EACL (AdaptNLP) 2021 Mandy Guo, Yinfei Yang, Daniel Cer, Qinlan Shen, Noah Constant

Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al., 2019). This dataset paper presents MultiReQA, a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets.

Information Retrieval Question Answering +1

LongT5: Efficient Text-To-Text Transformer for Long Sequences

1 code implementation15 Dec 2021 Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang

Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models.

Abstractive Text Summarization Long-range modeling +2

Large Dual Encoders Are Generalizable Retrievers

no code implementations15 Dec 2021 Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, Yinfei Yang

With multi-stage training, surprisingly, scaling up the model size brings significant improvement on a variety of retrieval tasks, especially for out-of-domain generalization.

Domain Generalization

A Simple and Effective Method To Eliminate the Self Language Bias in Multilingual Representations

1 code implementation EMNLP 2021 ZiYi Yang, Yinfei Yang, Daniel Cer, Eric Darve

A simple but highly effective method "Language Information Removal (LIR)" factors out language identity information from semantic related components in multilingual representations pre-trained on multi-monolingual data.

Cross-Lingual Transfer

Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

1 code implementation Findings (ACL) 2022 Jianmo Ni, Gustavo Hernández Ábrego, Noah Constant, Ji Ma, Keith B. Hall, Daniel Cer, Yinfei Yang

To support our investigation, we establish a new sentence representation transfer benchmark, SentGLUE, which extends the SentEval toolkit to nine tasks from the GLUE benchmark.

Contrastive Learning Semantic Textual Similarity +1

Pathdreamer: A World Model for Indoor Navigation

1 code implementation ICCV 2021 Jing Yu Koh, Honglak Lee, Yinfei Yang, Jason Baldridge, Peter Anderson

People navigating in unfamiliar buildings take advantage of myriad visual, spatial and semantic cues to efficiently achieve their navigation goals.

Semantic Segmentation Vision and Language Navigation

Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision

2 code implementations11 Feb 2021 Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, YunHsuan Sung, Zhen Li, Tom Duerig

In this paper, we leverage a noisy dataset of over one billion image alt-text pairs, obtained without expensive filtering or post-processing steps in the Conceptual Captions dataset.

 Ranked #1 on Image Classification on VTAB-1k (using extra training data)

Cross-Modal Retrieval Fine-Grained Image Classification +4

Universal Sentence Representations Learning with Conditional Masked Language Model

no code implementations1 Jan 2021 ZiYi Yang, Yinfei Yang, Daniel M Cer, Jax Law, Eric Darve

This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora.

Language Modelling Masked Language Modeling +2

Universal Sentence Representation Learning with Conditional Masked Language Model

no code implementations EMNLP 2021 ZiYi Yang, Yinfei Yang, Daniel Cer, Jax Law, Eric Darve

This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora.

Language Modelling Masked Language Modeling +2

Text-to-Image Generation Grounded by Fine-Grained User Attention

no code implementations7 Nov 2020 Jing Yu Koh, Jason Baldridge, Honglak Lee, Yinfei Yang

Localized Narratives is a dataset with detailed natural language descriptions of images paired with mouse traces that provide a sparse, fine-grained visual grounding for phrases.

Text to image generation Text-to-Image Generation +1

Neural Passage Retrieval with Improved Negative Contrast

no code implementations23 Oct 2020 Jing Lu, Gustavo Hernandez Abrego, Ji Ma, Jianmo Ni, Yinfei Yang

In this paper we explore the effects of negative sampling in dual encoder models used to retrieve passages for automatic question answering.

Open-Domain Question Answering Passage Retrieval +2

Neural Retrieval for Question Answering with Cross-Attention Supervised Data Augmentation

no code implementations ACL 2021 Yinfei Yang, Ning Jin, Kuo Lin, Mandy Guo, Daniel Cer

Independently computing embeddings for questions and answers results in late fusion of information related to matching questions to their answers.

Data Augmentation Question Answering

Language-agnostic BERT Sentence Embedding

5 code implementations ACL 2022 Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Arivazhagan, Wei Wang

While BERT is an effective method for learning monolingual sentence embeddings for semantic similarity and embedding based transfer learning (Reimers and Gurevych, 2019), BERT based cross-lingual sentence embeddings have yet to be explored.

Language Modelling Masked Language Modeling +7

SueNes: A Weakly Supervised Approach to Evaluating Single-Document Summarization via Negative Sampling

1 code implementation13 May 2020 Forrest Sheng Bao, Hebi Li, Ge Luo, Minghui Qiu, Yinfei Yang, Youbiao He, Cen Chen

Canonical automatic summary evaluation metrics, such as ROUGE, focus on lexical similarity which cannot well capture semantics nor linguistic quality and require a reference summary which is costly to obtain.

Abstractive Text Summarization Document Embedding +3

MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models

1 code implementation5 May 2020 Mandy Guo, Yinfei Yang, Daniel Cer, Qinlan Shen, Noah Constant

Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al., 2019). This paper presents MultiReQA, anew multi-domain ReQA evaluation suite com-posed of eight retrieval QA tasks drawn from publicly available QA datasets.

Information Retrieval Question Answering

Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve

no code implementations CL (ACL) 2021 Oshin Agarwal, Yinfei Yang, Byron C. Wallace, Ani Nenkova

We examine these questions by contrasting the performance of several variants of LSTM-CRF architectures for named entity recognition, with some provided only representations of the context as features.

Named Entity Recognition

Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models

1 code implementation8 Apr 2020 Oshin Agarwal, Yinfei Yang, Byron C. Wallace, Ani Nenkova

We propose a method for auditing the in-domain robustness of systems, focusing specifically on differences in performance due to the national origin of entities.

Fairness Named Entity Recognition

ReQA: An Evaluation for End-to-End Answer Retrieval Models

1 code implementation WS 2019 Amin Ahmad, Noah Constant, Yinfei Yang, Daniel Cer

Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance.

Information Retrieval Question Answering

Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction

no code implementations NAACL 2019 Yinfei Yang, Oshin Agarwal, Chris Tar, Byron C. Wallace, Ani Nenkova

Experiments on a complex biomedical information extraction task using expert and lay annotators show that: (i) simply excluding from the training data instances predicted to be difficult yields a small boost in performance; (ii) using difficulty scores to weight instances during training provides further, consistent gains; (iii) assigning instances predicted to be difficult to domain experts is an effective strategy for task routing.

Review Helpfulness Prediction with Embedding-Gated CNN

no code implementations29 Aug 2018 Cen Chen, Minghui Qiu, Yinfei Yang, Jun Zhou, Jun Huang, Xiaolong Li, Forrest Bao

Product reviews, in the form of texts dominantly, significantly help consumers finalize their purchasing decisions.

Syntactic Patterns Improve Information Extraction for Medical Search

no code implementations NAACL 2018 Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, Byron Wallace

Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest.

Universal Sentence Encoder

22 code implementations29 Mar 2018 Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil

For both variants, we investigate and report the relationship between model complexity, resource consumption, the availability of transfer task training data, and task performance.

Conversational Response Selection Semantic Textual Similarity +6

Combining Lexical and Syntactic Features for Detecting Content-dense Texts in News

no code implementations3 Apr 2017 Yinfei Yang, Ani Nenkova

On manually annotated data, we compare the performance of domain-specific classifiers, trained on data only from a given news domain and a general classifier in which data from all four domains is pooled together.

Question Answering

Detecting (Un)Important Content for Single-Document News Summarization

no code implementations EACL 2017 Yinfei Yang, Forrest Sheng Bao, Ani Nenkova

We present a robust approach for detecting intrinsic sentence importance in news, by training on two corpora of document-summary pairs.

Document Summarization News Summarization

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