Papers with pipeline framework
Deep Learning for Conversational AI (N18-6)
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| Challenge: | Spoken Dialogue Systems (SDS) have great commercial potential . the advent of deep learning has led to significant advances in this area of NLP research . |
| Approach: | This tutorial will introduce researchers to the pipeline framework for modelling goal-oriented dialogue systems. |
| Outcome: | This tutorial will familiarise researchers with the latest advances in spoken dialogue systems . the aim of the course is to encourage dialogue research in the NLP community . |
MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs (2022.emnlp-demos)
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| Challenge: | Existing medical dialogue systems have the problems of weak scalability, insufficient knowledge, and poor controllability. |
| Approach: | They propose a medical conversational question-answering system based on the knowledge graph to improve scalability and controllability. |
| Outcome: | The proposed system can conduct knowledge-grounded dialogues with users, using a Chinese medical knowledge graph and a large-scale dataset. |
ReportGPT: Human-in-the-loop Verifiable Table-to-Text Generation (2024.emnlp-industry)
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| Challenge: | Recent advances in the quality and accessibility of large language models have precipitated a surge in user-facing tools for content generation. |
| Approach: | They propose a pipeline framework for verifiable human-in-the-loop table-to-text generation that is based on a domain specific language and a set of modules that use it as a representation for generating verifierable commentary. |
| Outcome: | The proposed framework learns from human feedback in real-time, needing only a few samples to improve performance. |
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging (D18-1)
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| 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. |
Character-Level Chinese Dependency Parsing via Modeling Latent Intra-Word Structure (2024.findings-acl)
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| Challenge: | Existing word-level dependency parsing methods in Chinese lack explicit word boundaries due to the lack of word boundaries. |
| Approach: | They propose to model latent internal structures within Chinese words by constrained Eisner algorithm . they propose to guarantee a single root for intra-word structures and establish inter-word dependencies . |
| Outcome: | The proposed model outperforms existing models on Chinese treebanks and shows that it can predict plausible intra-word structures. |
Masked Language Models Know Which are Popular: A Simple Ranking Strategy for Commonsense Question Answering (2022.findings-emnlp)
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| Challenge: | Empirical results show that pre-trained language models can improve the typical answer generation of GLMs. |
| Approach: | They propose a ranking strategy that exploits WordNet to train a ranker that picks out the most popular answers for commonsense questions. |
| Outcome: | The proposed ranking strategy is tested on a commonsense question answering (QA) dataset and on negative samples from WordNet. |
GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction (2021.findings-acl)
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| Challenge: | a funder name refers to an agency, organization, or program providing financial support for the research. |
| Approach: | They propose a funding sentence classifier and a relation extraction framework to extract grant information from scientific articles. |
| Outcome: | The proposed framework outperforms state-of-the-art BERT-based RE baselines against the PubMed Central and arXiv test sets. |
Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension (2023.acl-long)
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| Challenge: | Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversation scenes. |
| Approach: | They propose a one-stage end-to-end framework to bridge the information gap between decision-making and question generation in a global understanding manner. |
| Outcome: | The proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark. |
Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading (2023.findings-emnlp)
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| Challenge: | Recent research has explored how to improve the abilities of decision-making and question generation. |
| Approach: | They propose a pipeline framework that aligns the document and user-provided information in an explicit way, makes decisions using a lightweight many-to-many entailment reasoning module and generates follow-up questions based on the document. |
| Outcome: | The proposed framework achieves state-of-the-art in micro-accuracy and ranks the first place on the public leaderboard of the CMR benchmark dataset ShARC. |
Reduce Redundancy Then Rerank: Enhancing Code Summarization with a Novel Pipeline Framework (2024.lrec-main)
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| Challenge: | Existing code summarization models lack redundant tokens and are plagued by exposure bias. |
| Approach: | They propose a pipeline framework to reduce redundancy then rerank that eliminates redundant information in code representation space and a re-ranking model to select more suitable summary candidates. |
| Outcome: | The proposed framework overrides state-of-the-art approaches on six datasets from the CodeSearchNet benchmark. |