Papers with pipeline approach
Neural Speech Translation using Lattice Transformations and Graph Networks (D19-53)
Copied to clipboard
| Challenge: | Existing work on end-to-end systems bypass the need for intermediate representations, but this approach is limited in practical applications. |
| Approach: | They propose a lattice-tosequence model which uses lattics as encoders and graph networks to address two problems by applying latticae transformations and a neural model. |
| Outcome: | The proposed model beats pipeline approaches while being orders of magnitude faster than previous work. |
IMSurReal: IMS at the Surface Realization Shared Task 2019 (D19-63)
Copied to clipboard
| Challenge: | a system for shallow and deep completion is presented for the Surface Realization Shared Task 2019 . the system achieves state-of-the-art performance without using external data. |
| Approach: | They propose a surface realization system that takes five steps without external data . they perform detailed error analysis revealing correlation between word order freedom and difficulty . |
| Outcome: | The proposed system achieves state-of-the-art without external data . it achieves highest BLEU scores on tokenized text and human evaluation on four languages . |
Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech (2024.acl-short)
Copied to clipboard
| Challenge: | Direct dependency parsing of the speech signal is proposed as a way of incorporating prosodic information into the parser and bypassing the limitations of a pipeline approach. |
| Approach: | They propose to use graph-based parsing and sequence labeling based parses to integrate prosodic information into the parser and bypass limitations of pipeline approaches. |
| Outcome: | The proposed graph based approach outperforms a pipeline approach on a large treebank of spoken french, despite having 30% fewer parameters. |
On the Use of External Data for Spoken Named Entity Recognition (2022.naacl-main)
Copied to clipboard
| Challenge: | Named entity recognition (NER) tasks require large labeled datasets to perform . compared to prior work, relative improvements in F1 of up to 16% are found . |
| Approach: | They propose to use self-training, knowledge distillation, and transfer learning to learn SLU models . they compare pipeline and pipeline approaches to find out how to use external data . |
| Outcome: | The proposed models improve performance beyond pre-trained models in resource-constrained settings . the best baseline model is a pipeline approach, while the best performance is achieved by an E2E model. |
An Empirical Study of Pipeline vs. Joint approaches to Entity and Relation Extraction (2022.aacl-short)
Copied to clipboard
| Challenge: | Entity and Relation Extraction tasks are often compared to pipeline approaches . a recent study shows that joint approaches can produce comparable results . |
| Approach: | They propose to use two approaches to the Entity and Relation Extraction task to compare their performance. |
| Outcome: | The proposed approach outperforms the best pipeline model but improperly designed approaches may have poor performance. |
FEVER: a Large-scale Dataset for Fact Extraction and VERification (N18-1)
Copied to clipboard
| Challenge: | 185,445 claims generated by altering sentences from Wikipedia are verified without knowledge of the sentence they were derived from. |
| Approach: | They propose a publicly available dataset for verification against textual sources, FEVER: Fact Extraction and VERification. |
| Outcome: | The proposed dataset achieves 31.87% accuracy on labeling a claim accompanied by the correct evidence, compared to 50.91% if we ignore the evidence. |
Faithful and Plausible Natural Language Explanations for Image Classification: A Pipeline Approach (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing explanation methods for image classification struggle to provide faithful and plausible explanations for predictions. |
| Approach: | They propose a natural language explanation method that can be applied to any CNN-based classifier without altering its training process or affecting predictive performance. |
| Outcome: | The proposed method can be applied to any CNN-based classifier without altering its training process or affecting predictive performance. |
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging (D18-1)
Copied to clipboard
| Challenge: | Existing pipelines for fact-checking of textual sources are limited . fact- checking of text sources requires a large knowledge base to extract relevant information . |
| Approach: | They propose a neural ranker that dynamically selects sentences to improve evidence retrieval . they incorporate lexical tagging methods into the pipeline framework to simplify the tasks . |
| Outcome: | The proposed model outperforms the existing TF-IDF method on a large-scale fact extraction and verification dataset with speedup. |
Counter-Argument Generation by Attacking Weak Premises (2021.findings-acl)
Copied to clipboard
| Challenge: | a recent work explores the generation of counter-arguments by undermining one of its premises . identifying the argument's weak premises is key to effective countering, we hypothesize . |
| Approach: | They propose a pipeline approach that first assesses the argument's weak premises and generates a counter-argument undermining the weakest among them. |
| Outcome: | The proposed approach undermins arguments by attacking weak premises . human annotators favor the proposed approach over state-of-the-art approaches . |
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks (2022.acl-long)
Copied to clipboard
| Challenge: | Argument mining (AM) is a computational process that is used to analyze information in a debating system. |
| Approach: | They propose to use a large dataset to automate the manual process of debating . they propose to integrate claim extraction, stance classification and evidence extraction tasks . |
| Outcome: | The proposed tasks can extract claims, stances, evidence and more from a large dataset . the proposed tasks are highly efficient and can be applied to argument mining tasks . |
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis (2024.eacl-long)
Copied to clipboard
Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan
| Challenge: | Existing methods to improve few-shot performance in aspect-based sentiment analysis (ABSA) require complex interactions between the target and the polarity of the sentiment. |
| Approach: | They propose a pipeline approach to construct a noisy ABSA dataset and adapt it to the ABSA tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art on the aspect extraction sentiment classification task and is capable of performing the harder aspect sentiment triplet extraction task. |
Textual Supervision for Visually Grounded Spoken Language Understanding (2020.findings-emnlp)
Copied to clipboard
| Challenge: | a new approach to spoken language understanding extracts semantic information directly from speech without relying on transcriptions. |
| Approach: | They propose to use textual supervision to train visually-grounded models of spoken language understanding without relying on transcriptions. |
| Outcome: | The proposed model improves when enough text is available, the study shows . compared with pipeline-based models, the pipeline approach performs better when enough data is available . |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
Copied to clipboard
Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
Revisiting the Negative Data of Distantly Supervised Relation Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods for relation extraction with distant supervision generate plenty of training samples but noisy labels and imbalanced training data cause problems. |
| Approach: | They propose a method that automatically labels a sentence with relational triples from a knowledge base. |
| Outcome: | The proposed method outperforms existing methods even with false positive samples. |
Phrase Retrieval for Open Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Open-Domain Conversational Question Answering (ODConvQA) aims to answer questions through a multi-turn conversation . however, such a pipeline approach makes the reader vulnerable to errors propagated from the retriever, which makes it slower since they are not runnable in parallel. |
| Approach: | They propose a method to directly predict answers with a phrase retrieval scheme for a sequence of words. |
| Outcome: | The proposed method outperforms the baselines on two ODConvQA datasets. |
Explain-Analyze-Generate: A Sequential Multi-Agent Collaboration Method for Complex Reasoning (2025.coling-main)
Copied to clipboard
| Challenge: | Multiagent debate (MAD) is a popular approach for large language models . however, the performance of LLMs is suboptimal in complex reasoning scenarios . |
| Approach: | They propose a sequential collaboration framework to enable agents to provide constructive assistance to peers by decomposing complex tasks into essential subtasks. |
| Outcome: | The proposed framework achieves the highest performance on 19 out of 23 tasks and lower costs compared to MAD. |
Simple and Effective Unsupervised Speech Translation (2023.acl-long)
Copied to clipboard
Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov, Yun Tang, Wei-Ning Hsu, Michael Auli, Juan Pino
| Challenge: | Existing methods to train speech models without labeled data are limited for most languages. |
| Approach: | They propose a pipeline approach to build speech translation systems without labeled data by leveraging recent advances in unsupervised speech recognition, machine translation and speech synthesis. |
| Outcome: | The proposed approach outperforms the state-of-the-art in unsupervised speech recognition by 3.2 BLEU on the Libri-Trans benchmark and the best supervised end-to-end models from only two years ago by an average of 5.0 BLUE over five X-En directions. |
Towards relation extraction from speech (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for extracting relations from speech have been neglected due to the nature of speech. |
| Approach: | They propose a listening information extraction task that uses speech to extract relation extraction from speech . they use a text-to-speech system and crowd-sourced native English speakers to test the task . |
| Outcome: | The proposed task extracts semantic relationships from speech data using a new model . the proposed task is more challenging than the existing method due to the characteristics of speech . |