Papers by Ajay Nagesh
Grounding Gradable Adjectives through Crowdsourcing (L18-1)
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| Challenge: | Often, texts describe interactions using vague, high-level language . crowdsourcing is expensive and requires extensive literature review and time . |
| Approach: | They propose a method for estimating concrete groundings for a set of gradable adjectives by crowdsourcing human intuitions and fitting a mixed effects model to the text. |
| Outcome: | The proposed model can generalize to unseen data and has a predictive R 2 of 0.632 in general and 0.677 on a subset of high-frequency adjectives. |
Keep Your Bearings: Lightly-Supervised Information Extraction with Ladder Networks That Avoids Semantic Drift (N18-2)
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| Challenge: | Using ladder networks, semi-supervised learning can be iterative and drifts semantically as learning progresses. |
| Approach: | They propose a method that uses ladder networks to perform a task of named entity classification using a large, unannotated dataset. |
| Outcome: | The proposed method improves on two standard datasets for named entity classification. |
Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models (N19-4)
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Rebecca Sharp, Adarsh Pyarelal, Benjamin Gyori, Keith Alcock, Egoitz Laparra, Marco A. Valenzuela-Escárcega, Ajay Nagesh, Vikas Yadav, John Bachman, Zheng Tang, Heather Lent, Fan Luo, Mithun Paul, Steven Bethard, Kobus Barnard, Clayton Morrison, Mihai Surdeanu
| Challenge: | a paper proposes a method for building probabilistic models of complex phenomena such as food insecurity . currently, these models are hand-built for each new situation and require months to construct . |
| Approach: | They propose an approach that builds executable probabilistic models from raw, free text. |
| Outcome: | The proposed approach builds executable probabilistic models from raw, free text. |
Visual Supervision in Bootstrapped Information Extraction (D18-1)
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| Challenge: | a list-based interface populated with informative samples is effective for data annotation . a 2D scatterplot populated by diverse and representative samples yields improved models . |
| Approach: | They propose a list-based interface that can be used to build efficient and effective data annotation models. |
| Outcome: | The proposed model learns the distributional similarity of entities through the patterns that match them in a large corpus while being discriminative with respect to human-labeled and machine-promoted entities. |
An Exploration of Three Lightly-supervised Representation Learning Approaches for Named Entity Classification (C18-1)
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| Challenge: | a recent study compares semi-supervised learning methods with bootstrapping methods . semi-semi-supervised methods reduce the amount of semantic drift introduced by iterative approaches . |
| Approach: | They propose to adapt three semi-supervised representation learning methods to an information extraction task . they show that all methods outperform state-of-the-art semi-representation learning methods . |
| Outcome: | The proposed methods outperform state-of-the-art semi-supervised methods on named entity classification task. |
Parallel Corpus Filtering via Pre-trained Language Models (2020.acl-main)
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| Challenge: | Existing methods to filter out noisy parallel sentences from web crawled data are in demand. |
| Approach: | They propose a method to filter out noisy sentence pairs from web crawled corpora using pre-trained language models. |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art on two datasets. |
MeetDot: Videoconferencing with Live Translation Captions (2021.emnlp-demo)
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Arkady Arkhangorodsky, Christopher Chu, Scot Fang, Yiqi Huang, Denglin Jiang, Ajay Nagesh, Boliang Zhang, Kevin Knight
| Challenge: | MeetDot is a videoconferencing system with live translation captions overlaid on screen . currently, the system supports speech and captions in 4 languages . |
| Approach: | They propose a videoconferencing system with live translation captions overlaid on screen . the system supports speech and captions in 4 languages and combines automatic speech recognition and machine translation in a cascade . |
| Outcome: | The proposed system supports speech and captions in 4 languages and has very tight latency requirements to have acceptable call quality. |