Korea, Republic of

State-of-the-art approaches for hate-speech detection usually exhibit poor performance in out-of-domain settings. This occurs, typically, due to classifiers overemphasizing source-specific information that negatively impacts its domain invariance. Prior work has attempted to penalize terms related to hate-speech from manually curated lists using feature attribution methods, which quantify the importance assigned to input terms by the classifier when making a prediction. We, instead, propose a domain adaptation approach that automatically extracts and penalizes source-specific terms using a domain classifier, which learns to differentiate between domains, and feature-attribution scores for hate-speech classes, yielding consistent improvements in cross-domain evaluation.

COLING 2022

Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection

feature attributions

hate speech

domain classification

domain adaptation

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

technical paper

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Reweighting Strategy based on Synthetic Data Identification for Sentence Similarity

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CoHS-CQG: Context and History Selection for Conversational Question Generation

Few-shot relation extraction aims to identify the relation type between entities in a given text in the low-resource scenario. Albeit much progress, existing meta-learning methods still fall into prediction confusions owing to the limited inference ability over shallow text features. To relieve these confusions, this paper proposes a discriminative rule-based knowledge (DRK) method. Specifically, DRK adopts a logic-aware inference module to ease the word-overlap confusion, which introduces a logic rule to constrain the inference process, thereby avoiding the adverse effect of shallow text features. Also, DRK employs a discrimination finding module to alleviate the entity-type confusion, which explores distinguishable text features via a hierarchical contrastive learning. We conduct extensive experiments on four types of meta tasks and the results show promising improvements from DRK (6.0% accuracy gains on average). Besides, error analyses reveal the word-overlap and entity-type errors are the main courses of mispredictions in few-shot relation extraction.

DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction

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DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting

This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification. DoubleMix first leverages a couple of simple augmentation operations to generate several perturbed samples for each training data, and then uses the perturbed data and original data to carry out a two-step interpolation in the hidden space of neural models. Concretely, it first mixes up the perturbed data to a synthetic sample and then mixes up the original data and the synthetic perturbed data. DoubleMix enhances models’ robustness by learning the
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DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification

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Enhancing Task-Specific Distillation in Small Data Regimes through Language Generation

Predicting difficulty of questions is crucial for technical interviews. However, such questions are long-form and more open-ended than factoid and multiple choice questions explored so far for question difficulty prediction. Existing models also require large volumes of candidate response data for training. We study weak supervision and use unsupervised algorithms for both question generation and difficulty prediction. We create a dataset of interview questions with difficulty scores for Deep Learning and use it to evaluate SOTA models for question difficulty prediction trained using weak supervision. Our analysis brings out the task’s
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A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions

A significant limitation in developing therapeutic chatbots to support people going through psychological distress is the lack of high-quality, large-scale datasets capturing conversations between clients and trained counselors. As a remedy, researchers have focused their attention on scraping conversational data from peer support platforms such as Reddit. But the extent to which the responses from peers align with responses from trained counselors is understudied. We address this gap by analyzing the differences between responses from counselors and peers by getting trained counselors to annotate ≈17K such responses using Motivational Interviewing Treatment Integrity (MITI) code, a well-established behavioral coding system that differentiates between favorable and unfavorable responses. We developed an annotation pipeline with several stages of quality control. Due to its design, this method was able to achieve 97% of coverage, meaning that out of the 17.3K responses we successfully labeled 16.8K with a moderate agreement. We use this data to conclude the extent to which conversational data from peer support platforms align with real therapeutic conversations and discuss in what ways they can be exploited to train therapeutic chatbots.

Curating a Large-Scale Motivational Interviewing Dataset using Peer Support Forums

This work deploys linguistically motivated features to classify paragraph-level text into fiction and non-fiction genre using a logistic regression model and infers lexical and syntactic properties that help distinguish the two genres.
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opposite pattern in non-fiction texts (inter-alia).

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Domain Classification-based Source-specific Term Penalization for Domain Adaptation in Hate-speech Detection

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