no code implementations • NAACL (SocialNLP) 2021 • Ivy Cao, Zizhou Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
As of now, however, it is not clear how and to what extent the pandemic has affected restaurant reviews, an analysis of which could potentially inform policies for addressing this ongoing situation.
no code implementations • 8 Sep 2024 • Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Second, we compare rule creation to individual instance labeling via active learning and demonstrate the importance of both across 6 datasets.
no code implementations • 9 May 2024 • Eden Shaveet, Crystal Su, Daniel Hsu, Luis Gravano
Restaurants are critical venues at which to investigate foodborne illness outbreaks due to shared sourcing, preparation, and distribution of foods.
no code implementations • 17 Oct 2023 • Matthew Toles, Yukun Huang, Zhou Yu, Luis Gravano
To enable evaluation of factual domain clarification question generation, We present a new task that focuses on the ability to elicit missing information in multi-hop reasoning tasks.
no code implementations • EMNLP (Louhi) 2020 • Ziyi Liu, Giannis Karamanolakis, Daniel Hsu, Luis Gravano
To improve performance without extra annotations, we create artificial training documents in the target language through machine translation and train mBERT jointly for the source (English) and target language.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Giannis Karamanolakis, Daniel Hsu, Luis Gravano
In this work, we propose a cross-lingual teacher-student method, CLTS, that generates "weak" supervision in the target language using minimal cross-lingual resources, in the form of a small number of word translations.
no code implementations • WS 2019 • Giannis Karamanolakis, Daniel Hsu, Luis Gravano
In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e. g., sentences) of a review may focus on different aspects of the entity in question.
1 code implementation • IJCNLP 2019 • Giannis Karamanolakis, Daniel Hsu, Luis Gravano
In this work, we consider weakly supervised approaches for training aspect classifiers that only require the user to provide a small set of seed words (i. e., weakly positive indicators) for the aspects of interest.
no code implementations • ICLR Workshop LLD 2019 • Giannis Karamanolakis, Daniel Hsu, Luis Gravano
In this work, we propose a weakly supervised approach for training neural networks for aspect extraction in cases where only a small set of seed words, i. e., keywords that describe an aspect, are available.