Distant finetuning with discourse relations for stance classification
- URL: http://arxiv.org/abs/2204.12693v1
- Date: Wed, 27 Apr 2022 04:24:35 GMT
- Title: Distant finetuning with discourse relations for stance classification
- Authors: Lifeng Jin, Kun Xu, Linfeng Song, Dong Yu
- Abstract summary: We propose a new method to extract data with silver labels from raw text to finetune a model for stance classification.
We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages.
Our approach ranks 1st among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater.
- Score: 55.131676584455306
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Approaches for the stance classification task, an important task for
understanding argumentation in debates and detecting fake news, have been
relying on models which deal with individual debate topics. In this paper, in
order to train a system independent from topics, we propose a new method to
extract data with silver labels from raw text to finetune a model for stance
classification. The extraction relies on specific discourse relation
information, which is shown as a reliable and accurate source for providing
stance information. We also propose a 3-stage training framework where the
noisy level in the data used for finetuning decreases over different stages
going from the most noisy to the least noisy. Detailed experiments show that
the automatically annotated dataset as well as the 3-stage training help
improve model performance in stance classification. Our approach ranks 1st
among 26 competing teams in the stance classification track of the NLPCC 2021
shared task Argumentative Text Understanding for AI Debater, which confirms the
effectiveness of our approach.
Related papers
- Detecting Statements in Text: A Domain-Agnostic Few-Shot Solution [1.3654846342364308]
State-of-the-art approaches usually involve fine-tuning models on large annotated datasets, which are costly to produce.
We propose and release a qualitative and versatile few-shot learning methodology as a common paradigm for any claim-based textual classification task.
We illustrate this methodology in the context of three tasks: climate change contrarianism detection, topic/stance classification and depression-relates symptoms detection.
arXiv Detail & Related papers (2024-05-09T12:03:38Z) - Annotation-Inspired Implicit Discourse Relation Classification with
Auxiliary Discourse Connective Generation [14.792252724959383]
Implicit discourse relation classification is a challenging task due to the absence of discourse connectives.
We design an end-to-end neural model to explicitly generate discourse connectives for the task, inspired by the annotation process of PDTB.
Specifically, our model jointly learns to generate discourse connectives between arguments and predict discourse relations based on the arguments and the generated connectives.
arXiv Detail & Related papers (2023-06-10T16:38:46Z) - Prefer to Classify: Improving Text Classifiers via Auxiliary Preference
Learning [76.43827771613127]
In this paper, we investigate task-specific preferences between pairs of input texts as a new alternative way for such auxiliary data annotation.
We propose a novel multi-task learning framework, called prefer-to-classify (P2C), which can enjoy the cooperative effect of learning both the given classification task and the auxiliary preferences.
arXiv Detail & Related papers (2023-06-08T04:04:47Z) - Task-Specific Embeddings for Ante-Hoc Explainable Text Classification [6.671252951387647]
We propose an alternative training objective in which we learn task-specific embeddings of text.
Our proposed objective learns embeddings such that all texts that share the same target class label should be close together.
We present extensive experiments which show that the benefits of ante-hoc explainability and incremental learning come at no cost in overall classification accuracy.
arXiv Detail & Related papers (2022-11-30T19:56:25Z) - Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs [37.754787051387034]
We propose a representation learning framework called breakpoint modeling.
Our approach trains models in an efficient and end-to-end fashion to build intermediate representations.
We show the benefit of our main breakpoint transformer, based on T5, over conventional representation learning approaches.
arXiv Detail & Related papers (2022-11-15T07:28:14Z) - Supporting Vision-Language Model Inference with Confounder-pruning Knowledge Prompt [71.77504700496004]
Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts.
To boost the transferability of the pre-trained models, recent works adopt fixed or learnable prompts.
However, how and what prompts can improve inference performance remains unclear.
arXiv Detail & Related papers (2022-05-23T07:51:15Z) - Towards Generalized Models for Task-oriented Dialogue Modeling on Spoken
Conversations [22.894541507068933]
This paper presents our approach to build generalized models for the Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations Challenge of DSTC-10.
We employ extensive data augmentation strategies on written data, including artificial error injection and round-trip text-speech transformation.
Our approach ranks third on the objective evaluation and second on the final official human evaluation.
arXiv Detail & Related papers (2022-03-08T12:26:57Z) - Speaker Embedding-aware Neural Diarization for Flexible Number of
Speakers with Textual Information [55.75018546938499]
We propose the speaker embedding-aware neural diarization (SEND) method, which predicts the power set encoded labels.
Our method achieves lower diarization error rate than the target-speaker voice activity detection.
arXiv Detail & Related papers (2021-11-28T12:51:04Z) - Probing Task-Oriented Dialogue Representation from Language Models [106.02947285212132]
This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks.
We fine-tune a feed-forward layer as the classifier probe on top of a fixed pre-trained language model with annotated labels in a supervised way.
arXiv Detail & Related papers (2020-10-26T21:34:39Z) - Noisy Self-Knowledge Distillation for Text Summarization [83.49809205891496]
We apply self-knowledge distillation to text summarization which we argue can alleviate problems with maximum-likelihood training.
Our student summarization model is trained with guidance from a teacher which generates smoothed labels to help regularize training.
We demonstrate experimentally on three benchmarks that our framework boosts the performance of both pretrained and non-pretrained summarizers.
arXiv Detail & Related papers (2020-09-15T12:53:09Z)
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.