Papers with pipeline approaches
Integrating Question Rewrites in Conversational Question Answering: A Reinforcement Learning Approach (2022.acl-srw)
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| Challenge: | Existing approaches to improve QR performance dependencies among dialogue history dependencies are limited. |
| Approach: | They propose a reinforcement learning approach that integrates QR and CQA tasks without corresponding labeled QR datasets. |
| Outcome: | The proposed approach improves existing pipeline approaches in conversational question answering (QA) existing methods depend on assumption of corresponding QR datasets for every CQA dataset, resulting in poor performance. |
Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision (2020.findings-emnlp)
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| Challenge: | Large pre-trained language models, such as BERT or RoBERTa, gain impressive results on a large variety of NLP tasks, including reasoning and inference. |
| Approach: | They propose a differentiable training framework to create models which output faithful rationales on a sentence level, by solely applying supervision on the target task. |
| Outcome: | The proposed model outperforms pipeline approaches and non-differentiable models on three different datasets while exceeding pipeline counterparts. |
Chinese Spoken Named Entity Recognition in Real-world Scenarios: Dataset and Approaches (2024.findings-acl)
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| Challenge: | Current Chinese Spoken NER datasets are laboratory-controlled and are limited in topics. |
| Approach: | They propose to use Chinese Spoken NER datasets to extract entities from speech to help voice assistants better grasp the intent behind user's questions and instructions. |
| Outcome: | The proposed methods improve on self-training-asr and mapping then distilling, and even compared with GPT4.0, they achieve better results. |
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks (D18-1)
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| Challenge: | a new approach for aspect-based sentiment analysis is proposed . we compare the performance of the proposed approach with pipeline approaches . |
| Approach: | They propose a model for aspect-based sentiment analysis that uses a convolutional neural network and fasttext embeddings to combine the two approaches. |
| Outcome: | The proposed model outperforms pipeline approaches in aspects-based sentiment analysis. |
End-to-end ASR to jointly predict transcriptions and linguistic annotations (2021.naacl-main)
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| Challenge: | Existing models generate audio transcripts by sequentially producing likely graphemes, or multi-graphemic units, from which lexical items of a language can be recovered. |
| Approach: | They propose a Transformer-based sequence-to-sequence model for automatic speech recognition that can produce high-quality transcriptions and linguistic annotations. |
| Outcome: | The proposed model can produce high-quality transcriptions and linguistic annotations on Japanese and English audio datasets. |
A-TASC: Asian TED-Based Automatic Subtitling Corpus (2025.acl-long)
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| Challenge: | Existing AS corpora and primary metric SubER focus on European languages. |
| Approach: | They propose an Asian TED-based automatic subtitling corpus derived from English TED Talks and a modification of SubER to enable reliable evaluation of subtitle quality for languages without explicit word boundaries. |
| Outcome: | The proposed corpus is based on TED Talks audio segments, transcripts, and subtitles in Chinese, Japanese, Korean, and Vietnamese. |
Controlling Text Complexity in Neural Machine Translation (D19-1)
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| Challenge: | Prior work on text complexity has focused on simplifying input text in one language, primarily English. |
| Approach: | They propose a method to align news articles written for different levels of target language proficiency. |
| Outcome: | The proposed model outperforms pipeline approaches that translate and simplify text independently. |
Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)
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| Challenge: | Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. |
| Approach: | They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach. |
| Outcome: | The proposed model improves performance on multiple datasets and compares with existing models. |
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. |
Neural Pipeline for Zero-Shot Data-to-Text Generation (2022.acl-long)
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| Challenge: | In data-to-text generation, training on in-domain data leads to overfitting and repeating training data noise. |
| Approach: | They propose to train pretrained language models on general-domain text-based operations by transforming single-item descriptions with modules trained on ordering, aggregation, and paragraph compression. |
| Outcome: | The proposed approach enables D2T generation from RDF triples in zero-shot settings. |
Where are we in Named Entity Recognition from Speech? (2020.lrec-1)
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| Challenge: | Named entity recognition is usually made through a pipeline process that consists of processing audio and applying a NER to the audio outputs. |
| Approach: | They propose an original 3-pass approach and explore the capability of an E2E system to do structured NER. |
| Outcome: | The proposed system performs better than the current pipeline approach. |
Multi-Domain Dialogue Acts and Response Co-Generation (2020.acl-main)
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| Challenge: | Existing pipeline approaches for task-oriented dialogue systems tend to predict multiple dialogue acts first and use them to assist response generation. |
| Approach: | They propose a neural co-generation model that generates dialogue acts and responses concurrently and preserves semantic structures of multi-domain dialogue acts. |
| Outcome: | The proposed model improves over state-of-the-art models in automatic and human evaluations on a large-scale dataset. |
Recognizing Everything from All Modalities at Once: Grounded Multimodal Universal Information Extraction (2024.findings-acl)
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| Challenge: | Existing studies on IE tasks have focused on recognizing and analyzing cross-modal information . a multimodal large language model (MLLM) is developed to analyze IE across modalities . |
| Approach: | They propose a multimodal large language model (MLLM) capable of grounding information from all modalities. |
| Outcome: | The proposed framework provides a framework to analyze IE tasks over various modalities and their fine-grained groundings. |