Word-order typology in Multilingual BERT: A case study in
subordinate-clause detection
- URL: http://arxiv.org/abs/2205.11987v1
- Date: Tue, 24 May 2022 11:35:39 GMT
- Title: Word-order typology in Multilingual BERT: A case study in
subordinate-clause detection
- Authors: Dmitry Nikolaev and Sebastian Pad\'o
- Abstract summary: In this paper, we use the task of subordinate-clause detection within and across languages to probe these properties.
We show that this task is deceptively simple, with easy gains offset by a long tail of harder cases, and that BERT's zero-shot performance is dominated by word-order effects.
- Score: 1.2129015549576372
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The capabilities and limitations of BERT and similar models are still unclear
when it comes to learning syntactic abstractions, in particular across
languages. In this paper, we use the task of subordinate-clause detection
within and across languages to probe these properties. We show that this task
is deceptively simple, with easy gains offset by a long tail of harder cases,
and that BERT's zero-shot performance is dominated by word-order effects,
mirroring the SVO/VSO/SOV typology.
Related papers
- A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision [74.972172804514]
We introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in a joint embedding space between signed language and spoken language text.
New dataset annotations provide continuous sign-level annotations for six hours of test videos, and will be made publicly available.
Our model significantly outperforms the previous state of the art on both tasks.
arXiv Detail & Related papers (2024-05-16T17:19:06Z) - Can BERT Refrain from Forgetting on Sequential Tasks? A Probing Study [68.75670223005716]
We find that pre-trained language models like BERT have a potential ability to learn sequentially, even without any sparse memory replay.
Our experiments reveal that BERT can actually generate high quality representations for previously learned tasks in a long term, under extremely sparse replay or even no replay.
arXiv Detail & Related papers (2023-03-02T09:03:43Z) - Prompting Language Models for Linguistic Structure [73.11488464916668]
We present a structured prompting approach for linguistic structured prediction tasks.
We evaluate this approach on part-of-speech tagging, named entity recognition, and sentence chunking.
We find that while PLMs contain significant prior knowledge of task labels due to task leakage into the pretraining corpus, structured prompting can also retrieve linguistic structure with arbitrary labels.
arXiv Detail & Related papers (2022-11-15T01:13:39Z) - Does BERT really agree ? Fine-grained Analysis of Lexical Dependence on
a Syntactic Task [70.29624135819884]
We study the extent to which BERT is able to perform lexically-independent subject-verb number agreement (NA) on targeted syntactic templates.
Our results on nonce sentences suggest that the model generalizes well for simple templates, but fails to perform lexically-independent syntactic generalization when as little as one attractor is present.
arXiv Detail & Related papers (2022-04-14T11:33:15Z) - Exploring the Role of BERT Token Representations to Explain Sentence
Probing Results [15.652077779677091]
We show that BERT tends to encode meaningful knowledge in specific token representations.
This allows the model to detect syntactic and semantic abnormalities and to distinctively separate grammatical number and tense subspaces.
arXiv Detail & Related papers (2021-04-03T20:40:42Z) - GiBERT: Introducing Linguistic Knowledge into BERT through a Lightweight
Gated Injection Method [29.352569563032056]
We propose a novel method to explicitly inject linguistic knowledge in the form of word embeddings into a pre-trained BERT.
Our performance improvements on multiple semantic similarity datasets when injecting dependency-based and counter-fitted embeddings indicate that such information is beneficial and currently missing from the original model.
arXiv Detail & Related papers (2020-10-23T17:00:26Z) - Intrinsic Probing through Dimension Selection [69.52439198455438]
Most modern NLP systems make use of pre-trained contextual representations that attain astonishingly high performance on a variety of tasks.
Such high performance should not be possible unless some form of linguistic structure inheres in these representations, and a wealth of research has sprung up on probing for it.
In this paper, we draw a distinction between intrinsic probing, which examines how linguistic information is structured within a representation, and the extrinsic probing popular in prior work, which only argues for the presence of such information by showing that it can be successfully extracted.
arXiv Detail & Related papers (2020-10-06T15:21:08Z) - A Study of Cross-Lingual Ability and Language-specific Information in
Multilingual BERT [60.9051207862378]
multilingual BERT works remarkably well on cross-lingual transfer tasks.
Datasize and context window size are crucial factors to the transferability.
There is a computationally cheap but effective approach to improve the cross-lingual ability of multilingual BERT.
arXiv Detail & Related papers (2020-04-20T11:13:16Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.