Papers with natural language process
Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs (2021.eacl-main)
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| Challenge: | Paraphrase generation requires many annotated paraphrase pairs, which are expensive to obtain. |
| Approach: | They propose a model that learns to disentangle the semantics and syntax of a sentence from unannotated texts. |
| Outcome: | The proposed model learns to disentangle the semantics and syntax of a sentence from a collection of unannotated texts. |
Achilles-Bench: A Challenging Benchmark for Low-Resource Evaluation (2024.findings-acl)
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| Challenge: | Existing low-resource datasets that challenge neural networks cause over-estimated performance, despite promising yet saturated results in high-res settings. |
| Approach: | They propose a benchmark Achilles-Bench to better evaluate the learning ability of neural networks in low-resource settings. |
| Outcome: | The proposed benchmarks show that even pre-trained language models show performance drops on NLP tasks. |
Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks (2025.coling-main)
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| Challenge: | Existing work uses large language models (LLMs) to evaluate natural language process tasks, but there are shortcomings in current LLMs. |
| Approach: | They examine the alignment between LLM evaluators and human annotators by comparing conventional and alignment tasks with different evaluation criteria. |
| Outcome: | The proposed models excel in general criteria, such as fluency, but face challenges with complex criteria, including numerical reasoning. |
AuriSRec: Adversarial User Intention Learning in Sequential Recommendation (2024.findings-emnlp)
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| Challenge: | Existing work focuses on capturing user implicit preferences from historical interactions and matching them with the next behavior, instead of predicting user explicit intentions. |
| Approach: | They propose an adversarial user intention learning approach for sequential recommendaiton . the approach explicitly predicts user current intentions by taking historical reviews as inputs . |
| Outcome: | The proposed approach explicitly predicts user intentions by inferring their decision-making process as explained in target reviews. |