Papers with run-time
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation (2023.emnlp-demo)
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Christian Huber, Tu Anh Dinh, Carlos Mullov, Ngoc-Quan Pham, Thai Binh Nguyen, Fabian Retkowski, Stefan Constantin, Enes Ugan, Danni Liu, Zhaolin Li, Sai Koneru, Jan Niehues, Alexander Waibel
| Challenge: | a framework to evaluate low-latency speech translations is currently only limited to specific aspects and is not able to compare different approaches. |
| Approach: | They propose a framework to perform and evaluate low-latency speech translation in realistic conditions. |
| Outcome: | The proposed framework evaluates various aspects of low-latency speech translation under realistic conditions. |
Mention Flags (MF): Constraining Transformer-based Text Generators (2021.acl-long)
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| Challenge: | Constrained decoding algorithms produce hypotheses satisfying all constraints, but they are computationally expensive and can lower the generated text quality. |
| Approach: | They propose a Mention Flag mechanism which traces whether lexical constraints are satisfied in outputs of an S2S decoder. |
| Outcome: | The proposed models maintain higher constraint satisfaction and text quality than baseline models and other constrained decoding algorithms. |
Agent Assist through Conversation Analysis (2020.emnlp-demos)
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Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using conversational approach to information retrieval for agent assistance, customer support agents are a critical part of an organization's customer support team. |
| Approach: | They propose a conversational approach to information retrieval for agent assistance that monitors an evolving conversation and recommends both responses and URLs of documents. |
| Outcome: | The proposed system monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. |
Optimizing Retrieval-augmented Reader Models via Token Elimination (2023.emnlp-main)
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| Challenge: | Existing methods for ODQA use a retrieval-augmented language model . a generative model can cause a significant bottleneck in decoding time . |
| Approach: | They propose to eliminate some of the retrieved information that might not contribute essential information to the answer generation process. |
| Outcome: | The proposed method reduces run-time by up to 62.2% with only 2% reduction in performance and improves performance. |
Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output (2024.emnlp-industry)
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| Challenge: | Existing methods for fact checking RAG outputs rely on large language models. |
| Approach: | They propose a method that computes a factuality score that can be thresholded to yield a binary decision to check RAG outputs. |
| Outcome: | The proposed method is low latency and low cost at run-time and no need for LLM fine-tuning. |
A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference Resolution. (2021.findings-emnlp)
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| Challenge: | Existing methods for Coreference Resolution rely on word embeddings for word representation, but performance of different embeddables is largely overlooked. |
| Approach: | They frame their study in the context of Event and Entity Coreference Resolution (EvCR & EnCR) they examine whether there is a trade-off between performance and embedding size . |
| Outcome: | The embeddings achieve 86% of the performance of the largest model while being 1.2% of its size. |
Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text Classification (2021.naacl-main)
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| Challenge: | Many text classification algorithms depend on the size of the corpus’ vocabulary due to their bag-of-words representation. |
| Approach: | They propose to evaluate how preprocessing techniques affect the run-time of models by evaluating ten techniques over four models and two datasets. |
| Outcome: | The proposed methods can reduce run-time with no loss of accuracy while sacrificing up to 65%. |
Enhancing Sequence-to-Sequence Neural Lemmatization with External Resources (2021.eacl-main)
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| Challenge: | a hybrid approach to lemmatization enhances the seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
| Approach: | They propose a hybrid approach that enhances a seq2seq neural model with additional lemmas extracted from an external lexicon or a rule-based system. |
| Outcome: | The proposed model achieves statistically significant improvements on 23 UD languages, compared to baseline models not utilizing additional lemma information. |
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction (2024.acl-long)
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| Challenge: | Existing methods for semantic parsing fail when hallucinations are encountered . QueryAgent solves a question step-by-step and performs stepwise self-correction . |
| Approach: | They propose a framework that solves a query step-by-step and performs stepwise self-correction. |
| Outcome: | The proposed framework outperforms existing methods on GrailQA and GraphQ by 5.7 and 15.0 points. |
SEEval: Advancing LLM Text Evaluation Efficiency and Accuracy through Self-Explanation Prompting (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have achieved remarkable success in various natural language generation tasks, but their performance in automatic text evaluation is not ready as human replacements. |
| Approach: | They propose a prompt-based text evaluator that incorporates self-explanation, a metacognitive strategy, to enhance automatic text evaluation. |
| Outcome: | The proposed method achieves competitive and often superior performance compared to the two state-of-the-art baselines – G-Eval and Analyze-Rate – and is 20 times more efficient in terms of run-time. |
Utilizing Large Twitter Corpora to Create Sentiment Lexica (L18-1)
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| Challenge: | Existing sentiment analysis systems only use word unigrams and bigrams, but lexicons using sentiment lexica are effective. |
| Approach: | They describe an automatic Twitter sentiment lexicon creator and a lexico-based sentiment analysis system. |
| Outcome: | The proposed system outperforms a manually annotated system in a comparison experiment. |
Quick Back-Translation for Unsupervised Machine Translation (2023.findings-emnlp)
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| Challenge: | Unsupervised machine translation models are limited by the run-time of autoregressive inference during back-translation and lack of synthetic data efficiency. |
| Approach: | They propose a two-for-one improvement to Transformer back-translation: Quick Back-Translation (QBT). QBT re-purposes the encoder as a generative model, and uses encoder-generated sequences to train the decoder. |
| Outcome: | Experiments on various WMT benchmarks show that QBT dramatically outperforms standard back-translation only method in terms of training efficiency for comparable translation qualities. |
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging (2021.emnlp-main)
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| Challenge: | despite popularity of influence functions, their computational cost does not scale well with model and training data size. |
| Approach: | They propose a fast parallel variant that approximates the “influences” of training data-points for test predictions. |
| Outcome: | The proposed method achieves about 80X speedup while being highly correlated with the original influence values. |