Papers with classification methods
FPC: Fine-tuning with Prompt Curriculum for Relation Extraction (2022.aacl-main)
Copied to clipboard
| Challenge: | Existing methods for relation extraction ignore semantics of relation labels . prompt-based fine-tuning has been proposed for RE . |
| Approach: | They propose a method for relation extraction using prompt-based fine-tuning . they use auxiliary prompt-tuned learning task to make the model capture semantics of relation labels . |
| Outcome: | The proposed method outperforms existing methods on four widely used RE benchmarks under fully supervised and low-resource settings. |
WAC: A Corpus of Wikipedia Conversations for Online Abuse Detection (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing methods for moderation of abusive content are limited by the lack of large corpora of conversations. |
| Approach: | They propose a framework with comment-level abuse annotations based on the Wikipedia Comment corpus . they propose 'context-based' approaches to detect abusive content based upon conversational context . |
| Outcome: | The proposed framework can be used to improve the moderation process of abusive content on the Internet. |
Hyperbolic Capsule Networks for Multi-Label Classification (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for classification of labels are limited by feature aggregation and encoding. |
| Approach: | They propose to use hyperbolic capsule networks to capture fine-grained label information . they also propose a new routing method to adaptively adjust capsule number during routing . |
| Outcome: | The proposed method significantly improves the performance of multi-label classification on tail labels. |
Distributional Term Set Expansion (L18-1)
Copied to clipboard
| Challenge: | Iterative term set expansion methods for distributional semantic models are used to label terms belonging to a sought after term set. |
| Approach: | They compare iterative term set expansion methods for distributional semantic models to the Simple Margin method, an active learning approach to classification using Support Vector Machines. |
| Outcome: | The proposed methods outperform centrality and classification based methods for distributional semantic models over five different term sets. |
Enhancing Writing Proficiency Classification in Developmental Education: The Quest for Accuracy (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing literature raises concerns about automated assessment tools like Accuplacer’s narrow representation of the writing process. |
| Approach: | They propose to use machine-learning to annotate college essays for machine/deep learning. |
| Outcome: | The proposed method improves the classification accuracy of 100 college-intending students’ essays against human raters. |
Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints. |
| Approach: | They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task. |
| Outcome: | The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. |
TESA: A Task in Entity Semantic Aggregation for Abstractive Summarization (2020.emnlp-main)
Copied to clipboard
| Challenge: | Abstractive summarization systems focus on paraphrasing and simplifying the source content, to the exclusion of such semantic abstraction capabilities. |
| Approach: | They propose a dataset and task to fine tune an abstractive summarization model to generate aggregations of 5.3K entities from a crowd-sourced dataset. |
| Outcome: | The proposed task and dataset show that the proposed model can generate aggregations at a semantic level, but that it is too complex to use. |
ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring (2021.emnlp-main)
Copied to clipboard
| Challenge: | Contracts are a common type of legal document that frequent in business workflows, but there has been limited NLP research in understanding and generating them. |
| Approach: | They propose a task of clause recommendation to help automate contract authoring . they first predict if a specific clause type is relevant to be added in a contract . then they propose two-staged pipeline to recommend top clauses based on the contract context . |
| Outcome: | The proposed pipeline predicts if a clause type is relevant to be added in a contract and recommends the top clauses for the given type based on the contract context. |
Humans Need Context, What about Machines? Investigating Conversational Context in Abusive Language Detection (2024.lrec-main)
Copied to clipboard
| Challenge: | In this paper, we examine the role of conversational context in abusive language detection . prior studies have ignored the contextual nature of abusive language, ignoring this aspect . toxicity, hate speech, harmful stereotypes are among the forms of harmful language . |
| Approach: | They propose to use conversational context to analyze abusive language detection using two methods . they use "abusive language" as an umbrella term to refer to various forms of harmful language . |
| Outcome: | The proposed approach is based on two datasets in English and a new dataset of French tweets annotated for hate speech and stereotypes. |