Papers with extraction task
Event Extraction as Multi-turn Question Answering (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Current approaches to event extraction fail to model rich interactions among event types and arguments of different roles. |
| Approach: | They propose a new paradigm that formulates event extraction as multi-turn question answering . they propose to use reading comprehension problems to extract triggers and arguments . |
| Outcome: | The proposed approach outperforms current state-of-the-art on argument extraction tasks . it makes full use of dependency among arguments and event types, and generalizes well . |
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction (2021.acl-short)
Copied to clipboard
| Challenge: | Recent work focuses on question answering based on machine reading comprehension . current approaches treat QA as extracting a consecutive piece of text to a given question. |
| Approach: | They propose a generative QA model that incorporates an extractive mechanism into a model. |
| Outcome: | The proposed model improves quality and semantic accuracy over baseline models. |
Transfer Learning from Semantic Role Labeling to Event Argument Extraction with Template-based Slot Querying (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for event argument extraction are limited by the limited amount of annotations available. |
| Approach: | They propose to use SRL annotations for event argument extraction . they propose to specify natural language-like queries to tackle label mismatch problem . |
| Outcome: | The proposed model achieves impressive zero-shot results on English benchmarks . it also provides benefits in low-resource cases, where few annotations are available . |
Learning to Extract Structured Entities Using Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Language Models (LMs) play a pivotal role in extracting structured information from unstructured text. |
| Approach: | They propose to reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives. |
| Outcome: | The proposed model outperforms baselines and human evaluations on the extracted entities. |
Extracting Shopping Interest-Related Product Types from the Web (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing e-commerce products are limited in their ability to assist customers in interest-oriented shopping. |
| Approach: | They propose to extract PTs from Web pages containing hand-crafted PT recommendations for SIs . they propose to use tree-transformer encoders for node classification to improve inter-node dependency modeling . |
| Outcome: | The proposed model outperforms the best baseline model by 2.37 F1 points on a WebPT dataset. |
EmoPrompt-ECPE: Emotion Knowledge-aware Prompt-tuning for Emotion-Cause Pair Extraction (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods for Emotion-cause pair extraction (ECPE) do not distinguish between the emotion-caused pairs that belong to different types of emotions, limiting their applicability. |
| Approach: | They propose an Emotion-cause pair extraction method which integrates the implicit knowledge of cause clauses into a prompt template and extends the emotion labels to categories with an external emotion word base. |
| Outcome: | The proposed method extracts all potential emotion clauses and corresponding cause clauses from unannotated documents. |
Representation Learning for Information Extraction from Form-like Documents (2020.acl-main)
Copied to clipboard
| Challenge: | Form-like documents like invoices, purchase orders, tax forms and insurance quotes are common in day-to-day business workflows, but current techniques for processing them largely still employ manual effort or brittle and error-prone heuristics for extraction. |
| Approach: | They propose an extraction system that uses knowledge of the types of the target fields to generate extraction candidates and a neural network architecture that learns a dense representation of each candidate based on neighboring words in the document. |
| Outcome: | The proposed system generates extraction candidates based on neighboring words in the document and is interpretable, as shown using loss cases. |
Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment Analysis (2024.findings-emnlp)
Copied to clipboard
| Challenge: | generative methods have shown promising results for extracting sentiment quadruplets . compound sentences can contain multiple quadroutlets, making extraction difficult . |
| Approach: | They propose an Aspect Term Oriented Sentence Splitter which simplifies compound sentences into simpler and clearer forms. |
| Outcome: | The proposed method outperforms existing methods in ASQP and ACOS tasks. |
Issues and Perspectives from 10,000 Annotated Financial Social Media Data (2020.lrec-1)
Copied to clipboard
| Challenge: | In the NLP community, many researchers have begun to use machine learning on financial and economic data. |
| Approach: | They present a dataset with 10,000 financial tweets annotated by experts from the front desk and the middle desk in a bank’s treasury. |
| Outcome: | The annotated financial tweets of a bank's front desk and middle desk are compared against a general sentiment dictionary and a domain-specific dictionary. |
GenLink: Generation-Driven Schema-Linking via Multi-Model Learning for Text-to-SQL (2025.emnlp-main)
Copied to clipboard
| Challenge: | Experimental results on BIRD and Spider benchmarks validate the effectiveness of GenLink. |
| Approach: | They propose a generation-driven schema-linking framework based on multi-model learning . experimental results validate the effectiveness of GenLink . |
| Outcome: | Experimental results show that GenLink improves schema-linking recall rate and cross-domain adaptability. |