Impact of Position Bias on Language Models in Token Classification Tasks

Core Concepts

Language models trained on datasets with skewed position distribution of classes or tags can suffer from position bias, leading to performance degradation on tokens appearing in out-of-distribution positions within sequences.

Abstract

The paper investigates the issue of position bias in transformer-based language models (LMs) when applied to token classification tasks such as named entity recognition (NER) and part-of-speech (POS) tagging.
Key highlights:

Datasets commonly used for NER (CoNLL03, OntoNotes5.0) and POS tagging (UD_en, TweeBank) exhibit skewed position distributions of classes/tags within sequences.
Evaluation on BERT and its variants shows that models trained on such datasets can suffer from position bias, with performance dropping for tokens appearing in positions beyond what the model was trained on.
The position bias is observed across different position embedding techniques, including absolute, relative, and rotary position embeddings.
To mitigate the position bias, the authors propose two methods: Random Position Perturbation and Context Perturbation, which introduce random shifts in token positions or concatenate sequences in random order during training.
Experiments show that the proposed methods can improve the model's robustness to position bias, leading to 2% gains in F1 score on the evaluated benchmarks.

Stats

The ratio of 'None' or O labeled tokens is 5 times as many for CoNLL03 and 8 times for Ontonotes5.0.
80% of sequences in CoNLL03, 74% of sequences in OntoNotes5.0, 82% of sequences in UD_en, and 86% of sequences in TweeBank are 25 words or shorter.
The distribution of named entity classes like PER and MISC in CoNLL03 exhibit right-skewness, with more than half of the instances appearing within the first 5 words.

Quotes

"Language Models (LMs) have shown state-of-the-art performance in Natural Language Processing (NLP) tasks. Downstream tasks such as Named Entity Recognition (NER) or Part-of-Speech (POS) tagging are known to suffer from data imbalance issues, particularly regarding the ratio of positive to negative examples and class disparities."
"To the best of our knowledge, the impact of position bias on token classification, like NER and POS tagging, has received less attention."
"Our analysis shows that when training on data with skewed position distribution, models are biased towards the first positions of a sequence, and the performance drops as the position of the word increases beyond what the model is trained on."

Key Insights Distilled From

Technical Report

by Mehd... at arxiv.org 04-09-2024

https://arxiv.org/pdf/2304.13567.pdf

Deeper Inquiries

How do the proposed debiasing methods, Random Position Perturbation and Context Perturbation, perform on other token classification tasks beyond NER and POS tagging

The proposed debiasing methods, Random Position Perturbation (RPP) and Context Perturbation (CP), can be applied to other token classification tasks beyond NER and POS tagging to improve the robustness of language models. These methods aim to mitigate position bias by introducing randomness in the position of tokens during training. By randomly shifting the position index of each token in an input sequence or by perturbing the context through different orderings of concatenated sequences, the models can learn to classify tokens over an unbiased distribution of positions within the input sequence. This approach can help the models generalize better to different positions within the sequences, reducing the impact of position bias on the model's performance in various token classification tasks.

What other factors, beyond position bias, could contribute to the performance degradation observed in the OntoNotes5.0 dataset when applying the debiasing methods

In addition to position bias, several other factors could contribute to the performance degradation observed in the OntoNotes5.0 dataset when applying the debiasing methods. Some of these factors include:

Class Imbalance: The dataset may have imbalanced class distributions, leading to challenges in learning from underrepresented classes and affecting the overall model performance.
Noise in Labels: Annotated data in the OntoNotes5.0 dataset may contain noisy or incorrect labels, which can introduce errors during training and impact the model's ability to generalize effectively.
Complexity of Named Entities: The dataset may contain complex named entities with varying lengths and structures, making it challenging for the model to accurately classify them, especially in positions where the context is ambiguous or unclear.
Contextual Ambiguity: Certain named entities or parts of speech may have ambiguous contexts, making it difficult for the model to correctly classify them based on position alone. This ambiguity can lead to performance degradation even after applying debiasing methods.

Considering these factors alongside position bias can provide a more comprehensive understanding of the challenges faced by the model in the OntoNotes5.0 dataset and help in devising strategies to improve model performance effectively.

How can the insights from this study on position bias be extended to improve the robustness of language models in other NLP applications, such as question answering or text generation, where position information is crucial

The insights from this study on position bias can be extended to improve the robustness of language models in other NLP applications where position information is crucial, such as question answering or text generation. Some ways to apply these insights include:

Question Answering: In question answering tasks, understanding the position bias can help in developing debiasing strategies to ensure that the model does not favor specific positions for answers. By incorporating techniques like Random Position Perturbation or Context Perturbation, models can be trained to provide more accurate answers regardless of their position in the context.
Text Generation: For text generation tasks, being aware of position bias can help in generating more coherent and contextually relevant text. By mitigating position bias through methods like Random Position Perturbation, models can learn to generate text that is not overly influenced by the position of tokens, leading to more natural and diverse outputs.

By applying the insights gained from studying position bias in token classification tasks to these NLP applications, researchers and practitioners can enhance the performance and robustness of language models across a wide range of tasks.