Semanlink - Active Learning for BERT: An Empirical Study

> **One-sentence Summary**: we suggest adding an unsupervised intermediate classification step, before finetunning and after pretraining BERT, and show it improves performance for data-constrained cases.

> for text classification cold start (when labeled
data is scarce), **add an intermediate unsupervised
classification task**, between the pretraining
and fine-tuning phases:
> perform clustering and
train the pre-trained model on predicting the
cluster labels.

> this additional
classification phase can significantly improve
performance, mainly for **topical classification**
tasks

> we use an efficient clustering technique,
that relies on simple Bag Of Words (BOW)
representations, to partition the unlabeled training
data into relatively homogeneous clusters of text
instances.
>
> Next, we treat these clusters as labeled
data for an intermediate text classification task, and
train the pre-trained model – with or without additional
MLM pretraining – with respect to this
multi-class problem, prior to the final fine-tuning
over the actual target-task labels

> The underlying
intuition is that inter-training the model
over a related text classification task would be more
beneficial compared to MLM inter-training, which
focuses on different textual entities, namely predicting
the identity of a single token.

2022-04-06 A propos

exBERT: Extending Pre-trained Models with Domain-specific Vocabulary Under Constrained Training Resources - ACL Anthology

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**Focus on the Embedding of Domain-specific Vocabulary.**

> exBERT
adds a new domain-specific vocabulary and the corresponding
embedding layer, as well as a small
extension module to the original unmodified model

> a pretraining
method allowing **low-cost embedding of
domain-specific vocabulary in the context of an
existing large pre-trained model such as BERT**

> exBERT... explicitly incorporates
the new domain’s vocabulary, while being able to
**reuse the original pre-trained model’s weights as is**
to reduce required computation and training data. Specifically, exBERT extends BERT by augmenting
its embeddings for the original vocabulary with
new embeddings for the domain-specific vocabulary
via **a learned small “extension” module**. **The
output of the original and extension modules are
combined via a trainable weighted sum operation**

In a way similar to concept developed in

> [[1902.00751] Parameter-Efficient Transfer Learning for NLP](doc:2021/04/1902_00751_parameter_efficien), but not in the fine-tuning paradigm.

[Github](https://github.com/cgmhaicenter/exBERT)

2021-04-11 A propos

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2020-07-02 A propos

[1904.08398] DocBERT: BERT for Document Classification

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2019-04-18 A propos