Search Results for author: Shoval Sadde

Found 6 papers, 2 papers with code

LM-Debugger: An Interactive Tool for Inspection and Intervention in Transformer-Based Language Models

1 code implementation26 Apr 2022 Mor Geva, Avi Caciularu, Guy Dar, Paul Roit, Shoval Sadde, Micah Shlain, Bar Tamir, Yoav Goldberg

The opaque nature and unexplained behavior of transformer-based language models (LMs) have spurred a wide interest in interpreting their predictions.

Large Scale Substitution-based Word Sense Induction

no code implementations ACL 2022 Matan Eyal, Shoval Sadde, Hillel Taub-Tabib, Yoav Goldberg

We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora.

Outlier Detection Word Embeddings +1

The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing

2 code implementations ACL 2021 Valentina Pyatkin, Shoval Sadde, Aynat Rubinstein, Paul Portner, Reut Tsarfaty

Modality is the linguistic ability to describe events with added information such as how desirable, plausible, or feasible they are.

Interactive Extractive Search over Biomedical Corpora

no code implementations WS 2020 Hillel Taub-Tabib, Micah Shlain, Shoval Sadde, Dan Lahav, Matan Eyal, Yaara Cohen, Yoav Goldberg

We present a system that allows life-science researchers to search a linguistically annotated corpus of scientific texts using patterns over dependency graphs, as well as using patterns over token sequences and a powerful variant of boolean keyword queries.

Syntactic Search by Example

no code implementations ACL 2020 Micah Shlain, Hillel Taub-Tabib, Shoval Sadde, Yoav Goldberg

A demo of the wikipedia system is available at: https://allenai. github. io/spike

What's Wrong with Hebrew NLP? And How to Make it Right

no code implementations IJCNLP 2019 Reut Tsarfaty, Amit Seker, Shoval Sadde, Stav Klein

For languages with simple morphology, such as English, automatic annotation pipelines such as spaCy or Stanford's CoreNLP successfully serve projects in academia and the industry.

Morphological Disambiguation

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