Taiwan

IJCNLP-AACL 2022

On the portability of extractive Question-Answering systems on scientific papers to real-life application scenarios

real-world scenarios

scientific papers

question answering

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, three amazing keynote events and two new initiatives of invited talks: Spotlight Talks for Young Rising Stars (STIRS) and The Next Big Idea Talks. Posters (including Findings of ACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop will have an oral session and a poster session as part of Poster Session 1. The program also features eight Tutorials and 28 Workshops. 

 
We wish you a wonderful conference! 
[**The ACL 2022 Organizing Committee**](https://www.2022.aclweb.org/organisers)
 
[**Conference Handbook**](https://drive.google.com/file/d/1_BUCMfhMVrjG9E2e71aHdHeE28KSje0l/view?usp=sharing) 
[**Mini Handbook**](https://drive.google.com/file/d/1qlBKl0wzmlVF1oCeMQl3BahLd9nLP5Ce/view?usp=sharing) 
[**Posters and Demo guides**](https://drive.google.com/file/d/1UucMAoCNncIOaH1rMMDa0owuG9qgvJTG/view?usp=sharing)

ACL 2022

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

workshop paper

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).

A registration is required to access this event. Access to the event site will be granted to all registered attendees on **November 16th**. If you have not already done so, please register using the link below:
 
 https://conference.iis.sinica.edu.tw/servlet/Register?ConferenceID=411

Registration Is Required

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

Utilizing citations for research artifacts (e.g., dataset, software) in scholarly papers contributes to efficient expansion of research artifact repositories and various applications e.g., the search, recommendation, and evaluation of such artifacts. This study focuses on citations using URLs (URL citations) and aims to identify and analyze research artifact citations automatically. We addressed the classification task for each URL citation to identify (1) the role that the referenced resources play in research activities, (2) the type of referenced resources, and (3) the reason why the author cited the resources. We propose the classification method using section titles and footnote texts as new input features. We extracted URL citations from international conference papers as experimental data. We performed 5-fold cross-validation using the data and computed the classification performance of our method. The results demonstrate that our method is effective in all tasks.

Classification of URL Citations in Scholarly Papers for Promoting Utilization of Research Artifacts

Current neural network solutions in scientific document processing employ models pretrained on domain-specific corpi, which are usually limited in model size, as pretraining can be costly and limited by training resources. We introduce a framework that uses data augmentation from such domain-specific pretrained models to transfer domain-specific knowledge to larger general pretrained models and im- prove performance on downstream tasks. Our method improves the performance of Named Entity Recognition in the astrophysical domain by more than 20% compared to domain-specific pretrained models finetuned to the target dataset.

Domain Specific Augmentations as Low Cost Teachers for Large Students

We investigate the problem of identifying the major hypothesis that is addressed in a scientific paper. To this end, we present a dataset from the domain of invasion biology that organizes a set of 954 papers into a network of fine-grained domain-specific categories of hypotheses. We carry out experiments on classifying abstracts according to these categories and present a pilot study on annotating hypothesis statements within the text. We find that hypothesis statements in our dataset are complex, varied and more or less explicit, and, importantly, spread over the whole abstract. Experiments with BERT-based classifiers show that these models are able to classify complex hypothesis statements to some extent, without being trained on sentence-level text span annotations.

Linking a Hypothesis Network From the Domain of Invasion Biology to a Corpus of Scientific Abstracts: The INAS Dataset

The most interesting words in scientific texts will often be novel or rare. This presents a challenge for scientific word embedding models to determine quality embedding vectors for useful terms that are infrequent or newly emerging. We demonstrate how latent semantic imputation (LSI) can address this problem by imputing embeddings for domain-specific words from up-to-date knowledge graphs while otherwise preserving the original word embedding model. We use the MeSH knowledge graph to impute embedding vectors for biomedical terminology without retraining and evaluate the resulting embedding model on a domain-specific word-pair similarity task. We show that LSI can produce reliable embedding vectors for rare and OOV terms in the biomedical domain.

Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation

Scientific research requires reading and extracting relevant information from existing scientific literature in an effective way. To gain insights over a collection of such scientific documents, extraction of entities and recognizing their types is considered to be one of the important tasks. Numerous studies have been conducted in this area of research. In our study, we introduce a framework for entity recognition and identification of NASA astrophysics dataset, which was published as a part of the DEAL SharedTask. We use a pre-trained multilingual model, based on a natural language processing framework for the given sequence labeling tasks. Experiments show that our model, Astro-mT5, outperforms the existing baseline in astrophysics related information extraction.

Astro-mT5: Entity Extraction from Astrophysics Literature using mT5 Language Model

Detecting Entities in the Astrophysics Literature (DEAL) is a proposed shared task in the scope of the first Workshop on Information Extraction from Scientific Publications (WIESP) at AACL-IJCNLP 2022. It aims to propose systems identifying astrophysical named entities. This article presents our system based on a majority voting strategy of an ensemble composed of 32 SciBERT models. The system we propose is ranked second and outperforms the baseline provided by the organisers by achieving an F1 score of 0.7993 and a Matthews Correlation Coefficient (MCC) score of 0.8978 in the testing phase.

A Majority Voting Strategy of a SciBERT-based Ensemble Models for Detecting Entities in the Astrophysics Literature (Shared Task)

Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent.

Full-Text Argumentation Mining on Scientific Publications

Tracking state-of-the-art (SOTA) results in machine learning studies is challenging due to high publication volume. Existing methods for creating leaderboards in scientific documents require significant human supervision or rely on scarcely available LaTeX source files. We propose Table Entity LINker (TELIN), a framework which extracts (task, model, dataset, metric) quadruples from collections of scientific publications in PDF format. TELIN identifies scientific named entities, constructs a knowledge base, and leverages human feedback to iteratively refine automatic extractions. TELIN identifies and prioritizes uncertain and impactful entities for human review to create a cascade effect for leaderboard completion. We show that TELIN is competitive with the SOTA but requires much less human annotation.

TELIN: Table Entity LINker for Extracting Leaderboards from Machine Learning Publications

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-tutorials.4/)


Grounding Meaning Representation for Situated Reasoning

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-tutorials.5/)


On the portability of extractive Question-Answering systems on scientific papers to real-life application scenarios

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