Papers with inference strategy
High Quality Rather than High Model Probability: Minimum Bayes Risk Decoding with Neural Metrics (2022.tacl-1)
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| Challenge: | Neural machine translations are ranked below human translations in professional evaluations . |
| Approach: | They apply minimum bayes risk decoding to optimize different metrics of translation quality . they show that model estimates and translation quality only vaguely correlate . |
| Outcome: | The proposed method improves human translations with different models and metric. |
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)
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| Challenge: | Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation. |
| Approach: | They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them. |
| Outcome: | The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches. |
Single Model Ensemble for Subword Regularized Models in Low-Resource Machine Translation (2022.findings-acl)
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| Challenge: | Existing subword regularizations use multiple segmentations during training but only use one segmentation in inference. |
| Approach: | They propose an inference strategy that uses multiple subword segmentations to solve this discrepancy in the training process and inference. |
| Outcome: | The proposed strategy reduces the cost of training and improves the performance of models trained with subword regularization in low-resource machine translation tasks. |
Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction (2020.findings-emnlp)
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| Challenge: | Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims to extract aspect terms and opinion terms from review text in the form of opinion pairs or opinion triplets. |
| Approach: | They propose a grid-based AFOE tagging scheme to address the task in an end-to-end fashion only with one unified grid tracking task. |
| Outcome: | The proposed tagging scheme outperforms baselines and achieves state-of-the-art performance. |
A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task . recent studies have focused on solving aspects term extraction, opinion term extraction and aspect-level sentiment classification tasks individually or in combination of two subtasks. |
| Approach: | They propose a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and improves performance . it can extract triplets of aspect terms, sentiments, and opinion terms from review sentences . |
The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis (2024.findings-acl)
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Miaoran Zhang, Vagrant Gautam, Mingyang Wang, Jesujoba Alabi, Xiaoyu Shen, Dietrich Klakow, Marius Mosbach
| Challenge: | In-context learning is a popular inference strategy where large language models solve a task using only a few labeled demonstrations without updating the model parameters. |
| Approach: | They conduct multidimensional analysis of multilingual in-context learning using 5 models from different model families and 9 datasets covering classification and generation tasks. |
| Outcome: | The results show that demonstrations vary significantly across models, tasks, and languages. |
Inference Strategies for Machine Translation with Conditional Masking (2020.emnlp-main)
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| Challenge: | Conditional masked language model training has proven successful for non-autoregressive and semi-auto-regressively sequence generation tasks. |
| Approach: | They propose a conditional masked language model (CMLM) that is a factorization of conditional probabilities of partial sequences and propose heuristics to improve performance. |
| Outcome: | The proposed algorithm is more efficient than the standard “mask-predict” algorithm on machine translation tasks. |
ArgU: A Controllable Factual Argument Generator (2023.acl-long)
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| Challenge: | Effective argumentation is essential towards a purposeful conversation with a satisfactory outcome. |
| Approach: | They propose a controllable neural argument generator capable of producing factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure. |
| Outcome: | The proposed model produces factual arguments from input facts and real-world concepts that can be explicitly controlled for stance and argument structure using Walton’s argument scheme-based control codes. |
Regression Aware Inference with LLMs (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks. |
| Approach: | They propose alternative inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses. |
| Outcome: | The proposed approach significantly improves over baselines across datasets and models. |
Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models (2026.acl-long)
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| Challenge: | Existing work on recurrent models for text embedding is limited to small task-specific models. |
| Approach: | They propose a vertically chunked inference strategy that enables fast embedding generation with memory usage that becomes constant in the input length once it exceeds the vertical chunk size. |
| Outcome: | The proposed architectures achieve competitive performance across benchmarks while maintaining a substantially smaller memory footprint compared to transformer-based models. |