Search Results for author: Thamme Gowda

Found 7 papers, 4 papers with code

Macro-Average: Rare Types Are Important Too

1 code implementation NAACL 2021 Thamme Gowda, Weiqiu You, Constantine Lignos, Jonathan May

While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy.

Information Retrieval Machine Translation +1

Many-to-English Machine Translation Tools, Data, and Pretrained Models

2 code implementations ACL 2021 Thamme Gowda, Zhao Zhang, Chris A Mattmann, Jonathan May

While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages.

Machine Translation Transfer Learning +1

Finding the Optimal Vocabulary Size for Neural Machine Translation

1 code implementation Findings of the Association for Computational Linguistics 2020 Thamme Gowda, Jonathan May

We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components.

Classification General Classification +2

Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining

no code implementations WS 2019 Xiaoman Pan, Thamme Gowda, Heng Ji, Jonathan May, Scott Miller

Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking.

Cross-Lingual Entity Linking Entity Linking

Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition

1 code implementation24 Oct 2019 Ninareh Mehrabi, Thamme Gowda, Fred Morstatter, Nanyun Peng, Aram Galstyan

We study the bias in several state-of-the-art named entity recognition (NER) models---specifically, a difference in the ability to recognize male and female names as PERSON entity types.

Named Entity Recognition NER

SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage

no code implementations ACL 2019 Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller

In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed.

Information Retrieval Machine Translation +1

Always Lurking: Understanding and Mitigating Bias in Online Human Trafficking Detection

no code implementations3 Dec 2017 Kyle Hundman, Thamme Gowda, Mayank Kejriwal, Benedikt Boecking

Web-based human trafficking activity has increased in recent years but it remains sparsely dispersed among escort advertisements and difficult to identify due to its often-latent nature.

Decision Making

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