Papers with AP
Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation (N18-2)
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| Challenge: | a tunable agent decides the best segmentation strategy for a user-defined BLEU loss and Average Proportion (AP) constraint. |
| Approach: | They propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and average proportion (AP) constraint. |
| Outcome: | The proposed agent outperforms existing Wait-if-diff and Wait-If-worse agents on BLEU with a lower latency. |
Reusable workflows for gender prediction (L18-1)
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| Challenge: | Existing systems for author profiling (AP) modeling require extensive feature engineering and testing. |
| Approach: | They propose to implement a system for author profiling (AP) modeling that reduces the complexity and time of building a sophisticated model for a number of different AP tasks. |
| Outcome: | The proposed model achieves comparable results to state of the art models for cross-genre gender prediction, but lags when genre of test set is different from genre of train set. |
Localizing Active Objects from Egocentric Vision with Symbolic World Knowledge (2023.emnlp-main)
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| Challenge: | Existing work on active object grounding from an egocentric perspective is focusing on localizing and tracking active objects that undergo major state change as a result of human actions/interactions to the environment without being told exactly what/where to ground. |
| Approach: | They propose to use a narrated egocentric video dataset to localize and track active objects that undergo major state change as a result of human actions/interactions to the environment without being told exactly what/where to ground. |
| Outcome: | The proposed framework leads to 54% improvement in standard metrics on the TREK-150-OPE-Det localization + tracking task, and >7% improvement in all standard metrics. |
Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks (2024.lrec-main)
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| Challenge: | Attention pruning techniques have been developed to identify and exploit sparseness . previous work has taken pioneering steps to discover and explain the sparsity in attention patterns . |
| Approach: | They propose a framework that observes attention patterns in a fixed dataset and generates a global sparseness mask. |
| Outcome: | The proposed approach saves 90% of computations and maintains quality of results. |
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification (D18-1)
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| Challenge: | Bank Question corpus is a corpus for sentence semantic equivalence identification (SSEI) because of rich expressions in natural languages, SSEI is really a challenging task. |
| Approach: | They propose to cluster 120,000 question pairs from 1-year online bank custom service logs into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation algorithm to achieve questions with the same intent. |
| Outcome: | The proposed method achieves questions with the same intent by clustering deduplicated questions into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm. |
From Capabilities to Performance: Evaluating Key Functional Properties of LLM Architectures in Penetration Testing (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have been explored for automating or enhancing penetration testing tasks, but their effectiveness and reliability remain open questions. |
| Approach: | They evaluate multiple LLM-based agents across realistic penetration testing scenarios . they also examine impact of core functional capabilities on agent success . |
| Outcome: | The proposed models improve agent performance in multi-step and real-time penetration testing scenarios. |
NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Models (2023.emnlp-main)
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| Challenge: | Temporal Logic (TL) can be used to specify complex high-level specifications for systems in many engineering domains. |
| Approach: | They propose a framework for translation between NL and TL using Large Language Models . they use a dataset to create a model with 23K NL-TL pairs and human annotation . |
| Outcome: | The proposed framework achieves higher accuracy (> 95%) using only 10% training data compared with baseline model. |
LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models (2025.findings-acl)
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| Challenge: | Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning. |
| Approach: | They propose to integrate large language models into AP and NLP planning frameworks by reviewing current research and identifying critical challenges and future directions. |
| Outcome: | The proposed frameworks are used to support reliable off-the-shelf AP planners. |