Papers with neural classifier
Mapping (Dis-)Information Flow about the MH17 Plane Crash (D19-50)
Copied to clipboard
| Challenge: | Digital media enables fast sharing of information, but also disinformation . studies on the spread of disinformation on social media focused on small, manually annotated datasets or used proxys for data annotation. |
| Approach: | They propose to use text classifiers to label Twitter content related to the MH17 crash to improve annotation accuracy. |
| Outcome: | The proposed classifier improves over a hashtag-based baseline, but still remains a challenge in labelling pro-Russian and pro-Ukrainian content with high precision. |
Bicleaner AI: Bicleaner Goes Neural (2022.lrec-1)
Copied to clipboard
| Challenge: | a new version of Bicleaner detects noisy sentences in parallel corpora . the tool is based on pre-trained transformer-based language models fine-tuned on a binary classification task. |
| Approach: | They propose to use Bicleaner AI to detect noisy sentences in parallel corpora . they use pre-trained transformer-based language models fine-tuned on a binary classification task . |
| Outcome: | The proposed tool improves translation quality and reduces manual cleaning steps. |
Transfer and Multi-Task Learning for Noun–Noun Compound Interpretation (D18-1)
Copied to clipboard
| Challenge: | In computational linguistics, nounnoun compound interpretation is approached as an automatic classification problem. |
| Approach: | They empirically evaluate the utility of transfer and multi-task learning on a challenging semantic classification task. |
| Outcome: | The proposed methods improve the accuracy of a neural classifier and its F1 scores on the less frequent, but more difficult relations. |
Exploring Predictive Uncertainty and Calibration in NLP: A Study on the Impact of Method & Data Scarcity (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Using low-resource languages, we assess the quality of uncertainty estimates from a wide array of approaches, but with more data. |
| Approach: | They train models on sub-sampled datasets in three different languages to assess the confidence of a neural classifier. |
| Outcome: | The proposed models train on sub-sampled datasets in three different languages and show that the quality of uncertainty estimates suffers with more data. |
Event-Driven News Stream Clustering using Entity-Aware Contextual Embeddings (2021.eacl-main)
Copied to clipboard
| Challenge: | a novel method for online news stream clustering is proposed . a user can scour the many news sources multiple times a day to find news articles . |
| Approach: | They propose a method for online news stream clustering that is a variant of the streaming K-means algorithm. |
| Outcome: | The proposed model achieves state-of-the-art on a standard stream clustering dataset of English documents. |
Multimodal Fact-Checking with Vision Language Models: A Probing Classifier based Solution with Embedding Strategies (2025.coling-main)
Copied to clipboard
| Challenge: | Existing fact-checking systems that use text and image information are susceptible to fake news spread by social media platforms. |
| Approach: | They propose a neural probing classifier based on multimodality and embeddings from text and image encoders to represent multimodal content for fact-checking. |
| Outcome: | The proposed classifier outperforms KNN and SVM baselines in leveraging extracted embeddings, highlighting its effectiveness for multimodal fact-checking. |
Denoising Multi-Source Weak Supervision for Neural Text Classification (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Recent years have witnessed the rapid development of deep neural networks (DNNs) for text classification problems. |
| Approach: | They propose a label denoiser which estimates the source reliability using a conditional soft attention mechanism and reduces label noise by aggregating rule-annotated weak labels. |
| Outcome: | The proposed model outperforms state-of-the-art methods on sentiment, topic, and relation classifications and achieves comparable performance with fully-supervised methods even without labeled data. |
LOPS: Learning Order Inspired Pseudo-Label Selection for Weakly Supervised Text Classification (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Weakly-supervised text classification methods are noisy due to their heuristic nature . selection of correct pseudo-labels has a huge potential for performance boost . |
| Approach: | They propose a pseudo-label selection method that takes learning order into account . they propose to select samples that are learnt earlier based on their pseudo-labels . |
| Outcome: | The proposed method is ineffective and unstable due to erroneous predictions from poorly calibrated models. |
ParlVote: A Corpus for Sentiment Analysis of Political Debates (2020.lrec-1)
Copied to clipboard
| Challenge: | Debate transcripts from the UK Parliament contain information about the positions taken by politicians towards important topics, but are difficult for humans to process manually. |
| Approach: | They propose to use a linear classifier and a transformer word embedding model to classify sentiment polarity in debate speeches to evaluate sentiment analysis systems for the political domain. |
| Outcome: | The proposed method performs better on the largest dataset and is more robust to other datasets. |
Unraveling the Mystery of Artifacts in Machine Generated Text (2022.lrec-1)
Copied to clipboard
| Challenge: | Recent studies show that human-written text is not distinguishable from synthetic text because of semantic errors or logical contradictions. |
| Approach: | They propose to analyze the forms of artifacts left by neural Text Generation Models by corrupting texts and replacing them with linguistic or statistical features. |
| Outcome: | The proposed method replaces text with linguistic or statistical features and improves the accuracy of the model. |
Identifying Source Language Expressions for Pre-editing in Machine Translation (2024.lrec-main)
Copied to clipboard
| Challenge: | MT-mediated communication can benefit from pre-editing source language texts to ensure accurate transmission of intended meaning in the target language. |
| Approach: | They hypothesize that such expressions tend to be distinctive features of texts originally written in the source language rather than translations generated from the target language into the source languages. |
| Outcome: | The proposed method identified characteristic expressions of the native language despite the noise and inherent nuances of the task. |
DEnsity: Open-domain Dialogue Evaluation Metric using Density Estimation (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent studies suggest that neural classifiers make overly confident predictions for examples from unseen distributions. |
| Approach: | They propose a new evaluation metric, DENSITY, which measures how likely a response would appear in the distribution of human conversations. |
| Outcome: | The proposed metric measures how likely a response would appear in the distribution of human conversations. |
SENTA: Sentence Simplification System for Slovene (2024.lrec-main)
Copied to clipboard
| Challenge: | Sentence simplification involves converting complex sentences into more accessible forms while preserving their meaning and context. |
| Approach: | They propose a system for sentence simplification in Slovene that uses a neural classifier to identify sentences that need simplification and a large Slovenen language model to refine sentences into a simpler form. |
| Outcome: | The proposed system achieves an excellent SARI score of 41 for a large Slovene language model based on T5 architecture . it is integrated into a freely accessible, user-friendly user interface, offering a valuable service to less-fluent Slovenen users. |