Papers with end-to-end pipeline
Learning Sparsity for Effective and Efficient Music Performance Question Answering (2025.acl-short)
Copied to clipboard
| Challenge: | Existing Music AVQA methods rely on dense and unoptimized representations, leading to inefficiencies in the isolation of key information, reduction of redundancy, and prioritization of critical samples. |
| Approach: | They propose a sparse learning framework specifically designed for Music AVQA to address these challenges. |
| Outcome: | The proposed framework reduces training time by 28.32% while maintaining accuracy while maintaining state-of-the-art performance on the Music AVQA datasets. |
Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems (2022.emnlp-industry)
Copied to clipboard
| Challenge: | Goal-oriented dialog systems fail to recognize the intent of natural language requests due to system errors, incomplete service coverage, or insufficient training. |
| Approach: | They propose an end-to-end pipeline for processing unrecognized user utterances, deployed in a commercial task-oriented dialog system, including a specifically-tailored clustering algorithm, a novel approach to cluster representative extraction, and cluster naming. |
| Outcome: | The proposed components show that they improve the performance of the proposed system in the analysis of unrecognized user requests. |
BIOPSY - Biomarkers In Oncology: Pipeline for Structured Yielding (2025.emnlp-industry)
Copied to clipboard
| Challenge: | Biomarkers are crucial indicators for early cancer detection and prognosis, but extracting biomarkers from clinical texts remains a challenge. |
| Approach: | They propose a pipeline that integrates a domain-adapted biomarker entity recognition model and a relation extraction model to link biomarkers to their respective mutations. |
| Outcome: | The proposed pipeline achieves an F1 score of 0.86 for oncology and 0.87 for neuroscience domains on 5,000 clinical texts. |
Automated Refugee Case Analysis: A NLP Pipeline for Supporting Legal Practitioners (2023.findings-acl)
Copied to clipboard
| Challenge: | In Canada, retrieving similar cases and their analysis is a key part of legal work . long processing times are due to a significant backlog and to the amount of work required from counsels . |
| Approach: | They propose to extend existing neural named-entity recognition models to retrieve 19 categories of items from refugee cases. |
| Outcome: | The proposed pipeline achieves a superior F1- score on five of the targeted categories and superior to 80% on an additional 4 categories. |
Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)
Copied to clipboard
Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen
| Challenge: | Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness. |
| Approach: | They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio. |
| Outcome: | The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model . |
See It All: Contextualized Late Aggregation for 3D Dense Captioning (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent approaches to 3D dense captioning struggle with contradicting objectives . SIA generates captions with different region of interest and aggregates them afterwards . |
| Approach: | They propose a transformer pipeline that engages in 3D dense captioning with a new paradigm . SIA decodes two sets of queries—context query and instance query—and then aggregates them afterwards . |
| Outcome: | The proposed pipeline generates captions with different region of interest and aggregates them afterwards to enhance local-global sensitivity. |
Learning to Decompose and Organize Complex Tasks (2021.naacl-main)
Copied to clipboard
| Challenge: | Using a novel end-to-end pipeline, we propose a solution that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Approach: | They propose a pipeline that consumes a complex task and induces 'dependency graphs' from unstructured text to represent sub-tasks and their relationships. |
| Outcome: | The proposed pipeline outperforms state-of-the-art graph induction pipelines in a dataset of complex tasks with their sub-task graphs. |
DynamicTOC: Persona-based Table of Contents for Consumption of Long Documents (2022.naacl-main)
Copied to clipboard
Himanshu Maheshwari, Nethraa Sivakumar, Shelly Jain, Tanvi Karandikar, Vinay Aggarwal, Navita Goyal, Sumit Shekhar
| Challenge: | Long documents are tedious to read through and can be authored by multiple entities . traditional document navigation is through a Table of Contents (ToC) but there is no way to highlight information relevant to different personas. |
| Approach: | They propose a dynamic table of content-based navigator that highlights sections of interest . DYNAMICTOC is augmented with short questions to assist users in understanding underlying content . |
| Outcome: | The proposed navigator highlights sections of interest in documents as per the aspects relevant to different personas. human and automatic evaluations suggest the efficacy of both end-to-end pipeline and different components. |
Infrastructure for Semantic Annotation in the Genomics Domain (2020.lrec-1)
Copied to clipboard
Mahmoud El-Haj, Nathan Rutherford, Matthew Coole, Ignatius Ezeani, Sheryl Prentice, Nancy Ide, Jo Knight, Scott Piao, John Mariani, Paul Rayson, Keith Suderman
| Challenge: | a novel infrastructure for biomedical text mining combines NLP and corpus linguistics methods to provide a comprehensive corpus for literature-based discovery. |
| Approach: | They propose a novel pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature . it uses an updatable Gene Ontology Semantic Tagger and a NLP pipeline scheduler to collect and process the corpus. |
| Outcome: | The proposed infrastructure allows for extreme-scale research on the open access PubMed Central archive. |
Efficient Inference for Large Vision-Language Models: Bottlenecks, Techniques, and Prospects (2026.findings-acl)
Copied to clipboard
Jun Zhang, Yicheng Ji, Feiyang Ren, Yihang Li, Bowen Zeng, Zonghao Chen, Ke Chen, Lidan Shou, Gang Chen, Huan Li
| Challenge: | Large Vision-Language Models are hindered by a systemic efficiency barrier known as visual token dominance. |
| Approach: | They propose a systematic taxonomy of efficiency techniques structured around the inference lifecycle . they examine visual encoding, prefilling, and decoding to understand bottlenecks . |
| Outcome: | The proposed techniques reveal how upstream decisions dictate downstream bottlenecks . the proposed techniques include hybrid compression and modality-aware decoding . |
Beyond WER: Probing Whisper’s Sub‐token Decoder Across Diverse Language Resource Levels (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large multilingual automatic speech recognition models achieve remarkable performance, but the internal mechanisms of the end-to-end pipeline remain underexplored. |
| Approach: | They propose to analyze Whisper's multilingual decoder to uncover systematic decoding disparities masked by aggregate error rates. |
| Outcome: | The proposed model performs better on higher resource languages, but lower resource languages fare worse on these metrics. |