Papers with evaluation scores
ConQuer: A Framework for Concept-Based Quiz Generation (2025.naacl-srw)
Copied to clipboard
| Challenge: | Existing concepts-based quiz generation frameworks that leverage external knowledge sources are challenging and labor intensive. |
| Approach: | They propose a concept-based quiz generation framework that leverages external knowledge sources to assess the quality of the generated quizzes, using LLMs as judges. |
| Outcome: | The proposed framework shows a 4.8% improvement in evaluation scores and a 77.52% win rate in pairwise comparisons against baseline quiz sets. |
CoPHE: A Count-Preserving Hierarchical Evaluation Metric in Large-Scale Multi-Label Text Classification (2021.emnlp-main)
Copied to clipboard
| Challenge: | Large-Scale Multi-Label Text Classification (LMTC) tasks with hierarchical label spaces include automatic assignment of ICD-9 codes to discharge summaries. |
| Approach: | They propose a set of metrics for hierarchical evaluation using the depth of the ontology to evaluate the predictions of neural LMTC models. |
| Outcome: | The proposed metrics compare with previous evaluations on prior art models for ICD-9 coding in MIMIC-III and propose further avenues of research involving the proposed representation. |
Global Optimization under Length Constraint for Neural Text Summarization (P19-1)
Copied to clipboard
| Challenge: | GOLC increases the probabilities of generating summaries that have high evaluation scores within a desired length. |
| Approach: | They propose a global optimization method under length constraint for neural text summarization models. |
| Outcome: | The proposed method generates fewer overlength summaries while maintaining the fastest processing speed. |
A Timestep aware Sentence Embedding and Acme Coverage for Brief but Informative Title Generation (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods for title generation are based on timestep aware sentence embeddings, but they are not effective for generating a title with appropriate information in the content. |
| Approach: | They propose a Timestep aware Sentence Embedding mechanism which refreshes the sentences’ embeddings with corresponding key words in different decoding timesteps. |
| Outcome: | The proposed framework outperforms existing methods on various title generation tasks and the evaluation scores are significantly higher than previous approaches. |
Where is this coming from? Making groundedness count in the evaluation of Document VQA models (2025.findings-naacl)
Copied to clipboard
| Challenge: | Document Visual Question Answering (VQA) models have come close to or matching human performance on some benchmarks. |
| Approach: | They propose a method that accounts for the semantic and multimodal groundedness of a model’s outputs and can be parameterized so that users can configure the score according to their preferences. |
| Outcome: | The proposed method produces scores that are a better indicator of a model’s robustness and tends to give higher rewards to better-calibrated answers. |
FloorPlan-LLaMa: Aligning Architects’ Feedback and Domain Knowledge in Architectural Floor Plan Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation methods for floor plan generation rely on statistical metrics like FID, GED, and PSNR, which fail to evaluate using domain knowledge. |
| Approach: | They propose to use a first floor plan dataset to train a floor plan generation model based on a multi-dimensional preference score and a textual analysis to integrate architects’ professional expertise and preferences. |
| Outcome: | The proposed model outperforms baseline models in text-conditional and class-condition tasks and is more rational and aligns better with human preferences. |
Multiple Text Style Transfer by using Word-level Conditional Generative Adversarial Network with Two-Phase Training (D19-1)
Copied to clipboard
| Challenge: | Generative adversarial network (GAN) is a popular model for text style transfer . but, training GAN often suffers from mode collapse problem, which causes that the transferred text is little related to the original text. |
| Approach: | They propose a non-parallel text style transfer model with a word-level conditional architecture and a two-phase training procedure to maintain style-unrelated words while changing others. |
| Outcome: | The proposed model outperforms state-of-the-art models on three real-world datasets in transfer accuracy and fluency. |
Effects of sub-word segmentation on performance of transformer language models (2023.emnlp-main)
Copied to clipboard
| Challenge: | Language models are a fundamental task in natural language processing, but few studies focus on the effect of sub-word segmentation on the performance of models. |
| Approach: | They compare GPT and BERT models trained with statistical segmentation algorithm BPE to unsupervised morphological segmentation algorithms Morfessor and StateMorph. |
| Outcome: | The proposed model trains for several languages and compares them with two unsupervised morphological segmentation algorithms. |
Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction (2020.coling-main)
Copied to clipboard
| Challenge: | Distant Supervision (DS) generates large-scale annotated data but has wrong labels that result in incorrect evaluation scores during testing. |
| Approach: | They build a dataset using DS-generated data as training data and hire annotators to label test data. |
| Outcome: | The proposed dataset NYTH has a much larger test set and performs more accurate and consistent evaluation. |
Maximum Score Routing For Mixture-of-Experts (2025.findings-acl)
Copied to clipboard
| Challenge: | Traditional mixture-of-experts (MoE) networks impose an expert capacity constraint to ensure GPU-friendly computation. |
| Approach: | They propose a routing paradigm that dynamically allocates input tokens to top-k experts through differentiable sparse transformations, enabling scalable model capacity while preserving computational efficiency. |
| Outcome: | The proposed model achieves lower training losses and higher evaluation scores at equivalent FLOPs compared to constrained and unconstrained baselines. |
Simul-COMET: A Quality Metric for Simultaneous Interpretation in Distant Language Pair Considering Word Order Difference (2026.findings-acl)
Copied to clipboard
| Challenge: | Simultaneous interpretation (SI) uses segmenting of source speech into chunks and translating them in order. |
| Approach: | They propose a variation of COMET that measures monotonicity for simultaneous interpretation . they train Simul-COMET on offline translation data and show stronger alignment with evaluation scores . |
| Outcome: | The proposed model shows stronger alignment with evaluation scores provided by interpreters than COMET. |