Semanlink - NLP event AND Arxiv Doc

> The most
successful previous approaches like InferSent (Conneau
et al., 2017), Universial Sentence Encoder
(USE) (Cer et al., 2018) and SBERT (Reimers and
Gurevych, 2019) heavily relied on labeled data to
train sentence embedding models.
>
> TSDAE can
achieve up to 93.1% of the performance of indomain
supervised approaches. Further, we
show that TSDAE is **a strong domain adaptation
and pre-training method for sentence
embeddings**, significantly outperforming other
approaches like Masked Language Model.

> During training, TSDAE
encodes corrupted sentences into fixed-sized
vectors and requires the decoder to reconstruct the
original sentences from this sentence embedding.

- <https://www.sbert.net/examples/unsupervised_learning/TSDAE/README.html>
- [github](https://github.com/UKPLab/sentence-transformers/tree/master/examples/unsupervised_learning/TSDAE)
- [UKPLab/sentence-transformers: Sentence Embeddings with BERT & XLNet](doc:2020/07/ukplab_sentence_transformers_s)
- [twitter](https://twitter.com/KexinWang2049/status/1433361957579538432):

> **TSDAE can learn domain-specific sentence embeddings with unlabeled sentences**
>
> Most importantly, instead of STS (Semantic Textual Similarity), **we suggest evaluating unsupervised sentence embeddings on the domain-specific tasks&datasets, which is the real use case for them**. Actually, STS scores do not correlate with performance on specific tasks.

2021-09-01 About

[2010.02353] Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages

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2021-08-25 About

[1807.04905] Ultra-Fine Entity Typing

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2021-06-22 About

[2106.04098] Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model

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2021-06-16 About

[2004.04906] Dense Passage Retrieval for Open-Domain Question Answering

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2021-06-03 About

[2009.12030] AutoETER: Automated Entity Type Representation for Knowledge Graph Embedding

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2021-05-17 About

[2011.05864] On the Sentence Embeddings from Pre-trained Language Models

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2021-04-19 About

[2010.01057] LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention

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2020-11-26 About

[1910.00163] Specializing Word Embeddings (for Parsing) by Information Bottleneck

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2020-06-29 About

[1909.04164] Knowledge Enhanced Contextual Word Representations

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2020-05-13 About

[1906.01195] Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

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2020-04-30 About

[1908.10084] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

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2019-08-28 About

[1905.07129] ERNIE: Enhanced Language Representation with Informative Entities

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2019-08-05 About

[1901.02860] Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

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2019-01-11 About

[1809.00782] Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text

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2018-09-06 About

[1601.03764] Linear Algebraic Structure of Word Senses, with Applications to Polysemy

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> Here it is shown that multiple word senses reside
in linear superposition within the word
embedding and simple sparse coding can recover
vectors that approximately capture the
senses

> Each extracted word sense is accompanied by one of about  2000 “discourse atoms” that gives a succinct description of which other words co-occur with that word sense.

> The success of the approach is mathematically explained using a variant of
the random walk on discourses model

("random walk": a generative model for language). Under the assumptions of this model,  there
exists a linear relationship between the vector of a
word w and the vectors of the words in its contexts (It is not the average of the words in w's context, but in a given corpus the matrix of the linear relationship does not depend on w. It can be estimated, and so we can compute the embedding of a word from the contexts it belongs to)

[Related blog post](/doc/?uri=https%3A%2F%2Fwww.offconvex.org%2F2016%2F07%2F10%2Fembeddingspolysemy%2F)

2018-08-28 About