Papers with neural method
Generating Sentential Arguments from Diverse Perspectives on Controversial Topic (D19-50)
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| Challenge: | ArgDiver model generates high-quality sentential arguments from multiple perspectives . retrieval-based systems do not have sufficient flexibility for input with missing keywords or topics unseen . |
| Approach: | They propose a neural method to generate sentential arguments from multiple perspectives . their model generates high-quality sentential argument, but shows higher diversity . |
| Outcome: | The proposed model generates high-quality sentential arguments from multiple perspectives . it shows that it can provide diverse perspectives on a controversial topic . |
Party Matters: Enhancing Legislative Embeddings with Author Attributes for Vote Prediction (P18-2)
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| Challenge: | Existing work on roll-call prediction limited to single session settings, thus not allowing for generalization across sessions. |
| Approach: | They propose a neural method that takes advantage of Congressional voting records to model voting behavior. |
| Outcome: | The proposed method achieves an average of 4% accuracy over the previous state-of-the-art. |
Multilingual Normalization of Temporal Expressions with Masked Language Models (2023.eacl-main)
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| Challenge: | Existing methods for normalizing temporal expressions are rule-based, which severely limits the applicability in multilingual settings. |
| Approach: | They propose a neural method for normalizing temporal expressions based on masked language modeling and a slot-based prediction scheme for context-independent representations. |
| Outcome: | The proposed method outperforms existing rule-based methods in many languages and in particular, for low-resource languages with performance improvements of up to 33 F1 on average compared to the state of the art. |
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information (P18-2)
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| Challenge: | Graph Convolutional Networks (GCNs) can extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Approach: | They propose a novel neural method to extract drug-drug interactions (DDIs) from texts using external drug molecular structure information. |
| Outcome: | The proposed model can extract drug-drug interactions (DDIs) from texts with high accuracy and the molecular information can enhance text-based extraction by 2.39 percent points in the F-score on the DDIExtraction 2013 shared task data set. |
Pedagogically Aligned Objectives Create Reliable Automatic Cloze Tests (2024.naacl-long)
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| Challenge: | a recent study has focused on the cloze training objective of Masked Language Models . distractors must be distinct and incorrect, and can be biased if the test creator is testing two versions of a text . |
| Approach: | They propose a method that jointly optimizes sets of distractors from Masked Language Models. |
| Outcome: | The proposed method has stronger correlation with teacher-created comprehension tests than state-of-the-art neural method and is more internally consistent. |
Enhancing Chinese Word Segmentation via Pseudo Labels for Practicability (2021.findings-acl)
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| Challenge: | Pre-trained language models alleviate segmentation ambiguity and out-of-vocabulary (OOV) words. |
| Approach: | They propose a semisupervised neural method which distills knowledge from unlabeled data to a student model to improve both in-domain and out-of-domain CWS. |
| Outcome: | The proposed method can keep practicability of the lightweight student model and improve segmentation effectively on downstream Chinese NLP tasks. |
Topic Detection and Tracking with Time-Aware Document Embeddings (2024.lrec-main)
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| Challenge: | Topic Detection and Tracking (TDT) systems aim to cluster news articles into real-world events. |
| Approach: | They propose a neural method that fuses temporal and textual information into a single representation of news documents for event detection. |
| Outcome: | The proposed model outperforms baselines on two benchmark TDT data sets in English. |