Semanlink - Arxiv Doc AND Yoav Goldberg

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

[2106.10199] BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

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2022-09-01 About

[2106.04612] Neural Extractive Search

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how to extend a
search paradigm we call “**extractive search**” with
neural similarity techniques.

> some information needs require extracting
and aggregating sub-sentence information
(words, phrases, or entities) from multiple documents
(e.g. a list of all the risk factors for a specific
disease and their number of mentions, or a comprehensive
table of startups and CEOs).

> extractive search combines
document selection with information extraction. **The query is extended with capture slots**:
these are **search terms that act as variables, whose
values should be extracted**.
> The user
is then presented with the matched documents, each
annotated with the corresponding captured spans,
as well as aggregate information over the captured
spans

Conclusion :

> We presented a system for neural extractive search.
While we found our system to be useful for scientific
search, it also has clear limitations and areas
for improvement, both in terms of accuracy (only
72.2% of the returned results are relevant, both the
alignment and similarity models generalize well to
some relations but not to others), and in terms of
scale

[Video of demo](https://www.youtube.com/watch?v=TtqWi2GgB5A&t=1832s)

2021-06-23 About

[2104.10809] Provable Limitations of Acquiring Meaning from Ungrounded Form: What will Future Language Models Understand?

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2021-05-23 About

[1608.05426] A Strong Baseline for Learning Cross-Lingual Word Embeddings from Sentence Alignments

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2018-07-23 About