Search Results for author: Yichao Lu

Found 21 papers, 5 papers with code

Weakly Supervised Extractive Summarization with Attention

no code implementations SIGDIAL (ACL) 2021 Yingying Zhuang, Yichao Lu, Simi Wang

Automatic summarization aims to extract important information from large amounts of textual data in order to create a shorter version of the original texts while preserving its information.

Extractive Summarization

Learning to Transfer for Traffic Forecasting via Multi-task Learning

1 code implementation27 Nov 2021 Yichao Lu

In particular, we present a multi-task learning framework for temporal and spatio-temporal domain adaptation of traffic forecasting models.

Domain Adaptation Multi-Task Learning

Pretrain-Finetune Based Training of Task-Oriented Dialogue Systems in a Real-World Setting

no code implementations NAACL 2021 Manisha Srivastava, Yichao Lu, Riley Peschon, Chenyang Li

In this work, we present a method for training retrieval-based dialogue systems using a small amount of high-quality, annotated data and a larger, unlabeled dataset.

Task-Oriented Dialogue Systems

Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings

no code implementations15 Oct 2020 Phillip Keung, Julian Salazar, Yichao Lu, Noah A. Smith

We then improve an XLM-based unsupervised neural MT system pre-trained on Wikipedia by supplementing it with pseudo-parallel text mined from the same corpus, boosting unsupervised translation performance by up to 3. 5 BLEU on the WMT'14 French-English and WMT'16 German-English tasks and outperforming the previous state-of-the-art.

14 Machine Translation +2

The Multilingual Amazon Reviews Corpus

no code implementations EMNLP 2020 Phillip Keung, Yichao Lu, György Szarvas, Noah A. Smith

We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification.

Classification General Classification +4

Efficient and Information-Preserving Future Frame Prediction and Beyond

1 code implementation ICLR 2020 Wei Yu, Yichao Lu, Steve Easterbrook, Sanja Fidler

Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios.

Frame Object Detection +2

Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings

no code implementations EMNLP 2020 Phillip Keung, Yichao Lu, Julian Salazar, Vikas Bhardwaj

Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source language and evaluated on a different target language.

Model Selection Transfer Learning +1

Attentional Speech Recognition Models Misbehave on Out-of-domain Utterances

1 code implementation12 Feb 2020 Phillip Keung, Wei Niu, Yichao Lu, Julian Salazar, Vikas Bhardwaj

We discuss the problem of echographic transcription in autoregressive sequence-to-sequence attentional architectures for automatic speech recognition, where a model produces very long sequences of repetitive outputs when presented with out-of-domain utterances.

Automatic Speech Recognition Frame

Learning Effective Visual Relationship Detector on 1 GPU

no code implementations12 Dec 2019 Yichao Lu, Cheng Chang, Himanshu Rai, Guangwei Yu, Maksims Volkovs

We present our winning solution to the Open Images 2019 Visual Relationship challenge.

Object Detection

CrevNet: Conditionally Reversible Video Prediction

no code implementations25 Oct 2019 Wei Yu, Yichao Lu, Steve Easterbrook, Sanja Fidler

Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios.

Video Prediction

Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting

no code implementations NAACL 2019 Yichao Lu, Manisha Srivastava, Jared Kramer, Heba Elfardy, Andrea Kahn, Song Wang, Vikas Bhardwaj

To test our models, a customer service agent handles live contacts and at each turn we present the top four model responses and allow the agent to select (and optionally edit) one of the suggestions or to type their own.

Response Generation

A practical approach to dialogue response generation in closed domains

no code implementations28 Mar 2017 Yichao Lu, Phillip Keung, Shaonan Zhang, Jason Sun, Vikas Bhardwaj

We describe a prototype dialogue response generation model for the customer service domain at Amazon.

Response Generation

Finding Linear Structure in Large Datasets with Scalable Canonical Correlation Analysis

no code implementations26 Jun 2015 Zhuang Ma, Yichao Lu, Dean Foster

In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computationally and storage expensive.

Stochastic Optimization

Large scale canonical correlation analysis with iterative least squares

no code implementations NeurIPS 2014 Yichao Lu, Dean P. Foster

Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems.

Fast Ridge Regression with Randomized Principal Component Analysis and Gradient Descent

no code implementations15 May 2014 Yichao Lu, Dean P. Foster

We propose a new two stage algorithm LING for large scale regression problems.

Faster Ridge Regression via the Subsampled Randomized Hadamard Transform

no code implementations NeurIPS 2013 Yichao Lu, Paramveer Dhillon, Dean P. Foster, Lyle Ungar

We propose a fast algorithm for ridge regression when the number of features is much larger than the number of observations ($p \gg n$).

New Subsampling Algorithms for Fast Least Squares Regression

no code implementations NeurIPS 2013 Paramveer Dhillon, Yichao Lu, Dean P. Foster, Lyle Ungar

We address the problem of fast estimation of ordinary least squares (OLS) from large amounts of data ($n \gg p$).

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