Search Results for author: Shuo Sun

Found 18 papers, 5 papers with code

CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval

no code implementations EMNLP 2020 Shuo Sun, Kevin Duh

We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia.

Cross-Lingual Information Retrieval

BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task

no code implementations WMT (EMNLP) 2020 Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Vishrav Chaudhary, Mark Fishel, Francisco Guzmán, Lucia Specia

We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems.

DeepScalper: A Risk-Aware Deep Reinforcement Learning Framework for Intraday Trading with Micro-level Market Embedding

no code implementations15 Dec 2021 Shuo Sun, Rundong Wang, Xu He, Junlei Zhu, Jian Li, Bo An

However, it is hard to apply existing RL methods to intraday trading due to the following three limitations: 1) overlooking micro-level market information (e. g., limit order book); 2) only focusing on local price fluctuation and failing to capture the overall trend of the whole trading day; 3) neglecting the impact of market risk.

Algorithmic Trading reinforcement-learning

Reinforcement Learning for Quantitative Trading

no code implementations28 Sep 2021 Shuo Sun, Rundong Wang, Bo An

RL's impact is pervasive, recently demonstrating its ability to conquer many challenging QT tasks.

Decision Making reinforcement-learning

Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications

no code implementations EMNLP 2021 Shuo Sun, Ahmed El-Kishky, Vishrav Chaudhary, James Cross, Francisco Guzmán, Lucia Specia

Sentence-level Quality estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels.

Machine Translation Model Compression +1

An Exploratory Study on Multilingual Quality Estimation

no code implementations Asian Chapter of the Association for Computational Linguistics 2020 Shuo Sun, Marina Fomicheva, Fr{\'e}d{\'e}ric Blain, Vishrav Chaudhary, Ahmed El-Kishky, Adithya Renduchintala, Francisco Guzm{\'a}n, Lucia Specia

Predicting the quality of machine translation has traditionally been addressed with language-specific models, under the assumption that the quality label distribution or linguistic features exhibit traits that are not shared across languages.

Machine Translation Translation

CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task

1 code implementation ACL 2020 Shuo Sun, Suzanna Sia, Kevin Duh

We present CLIReval, an easy-to-use toolkit for evaluating machine translation (MT) with the proxy task of cross-lingual information retrieval (CLIR).

Cross-Lingual Information Retrieval Document Translation +2

Are we Estimating or Guesstimating Translation Quality?

no code implementations ACL 2020 Shuo Sun, Francisco Guzm{\'a}n, Lucia Specia

Recent advances in pre-trained multilingual language models lead to state-of-the-art results on the task of quality estimation (QE) for machine translation.

Machine Translation Translation

Unsupervised Quality Estimation for Neural Machine Translation

3 code implementations21 May 2020 Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia

Quality Estimation (QE) is an important component in making Machine Translation (MT) useful in real-world applications, as it is aimed to inform the user on the quality of the MT output at test time.

Machine Translation Translation

Modeling Document Interactions for Learning to Rank with Regularized Self-Attention

no code implementations8 May 2020 Shuo Sun, Kevin Duh

Learning to rank is an important task that has been successfully deployed in many real-world information retrieval systems.

Information Retrieval Learning-To-Rank

Spectrally reconfigurable quantum emitters enabled by optimized fast modulation

no code implementations27 Mar 2020 Daniil M. Lukin, Alexander D. White, Rahul Trivedi, Melissa A. Guidry, Naoya Morioka, Charles Babin, Öney O. Soykal, Jawad Ul Hassan, Nguyen Tien Son, Takeshi Ohshima, Praful K. Vasireddy, Mamdouh H. Nasr, Shuo Sun, Jean-Phillipe W. MacLean, Constantin Dory, Emilio A. Nanni, Jörg Wrachtrup, Florian Kaiser, Jelena Vučković

To enable the experimental demonstration of this spectral control scheme, we investigate the Stark tuning properties of the silicon vacancy in silicon carbide, a color center with promise for optical quantum information processing technologies.

Quantum Physics Optics

WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

6 code implementations EACL 2021 Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong, Francisco Guzmán

We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of Wikipedia articles in 85 languages, including several dialects or low-resource languages.

Sentence Embeddings

Value Functions for Depth-Limited Solving in Zero-Sum Imperfect-Information Games

no code implementations31 May 2019 Vojtěch Kovařík, Dominik Seitz, Viliam Lisý, Jan Rudolf, Shuo Sun, Karel Ha

We provide a formal definition of depth-limited games together with an accessible and rigorous explanation of the underlying concepts, both of which were previously missing in imperfect-information games.

Cross-Lingual Learning-to-Rank with Shared Representations

no code implementations NAACL 2018 Shota Sasaki, Shuo Sun, Shigehiko Schamoni, Kevin Duh, Kentaro Inui

Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{'}s query.

Cross-Lingual Information Retrieval Learning-To-Rank +1

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