Taiwan

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.

IJCNLP-AACL 2022

Domain Specific Augmentations as Low Cost Teachers for Large Students

teacher-student models

data augmentation

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

workshop paper

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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). 

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

Topic models represent groups of documents as
a list of words (the topic labels). This work asks
whether an alternative approach to topic labeling
can be developed that is closer to a natural
language description of a topic than a word list.
To this end, we present an approach to generating
human-like topic labels using abstractive
multi-document summarization (MDS). We investigate
our approach with an exploratory case
study. We model topics in citation sentences
in order to understand what further research
needs to be done to fully operationalize MDS
for topic labeling. Our case study shows that
in addition to more human-like topics there are
additional advantages to evaluation by using
clustering and summarization measures instead
of topic model measures. However, we find that
there are several developments needed before
we can design a well-powered study to evaluate
MDS for topic modeling fully. Namely,
improving cluster cohesion, improving the factuality
and faithfulness of MDS, and increasing
the number of documents that might be supported
by MDS. We present a number of ideas
on how these can be tackled and conclude with
some thoughts on how topic modeling can also
be used to improve MDS in general.

Moving beyond word lists: towards abstractive topic labels for human-like topics of scientific documents

As the rate of scientific output continues to grow, it is increasingly important to develop systems to improve interfaces between researchers and scholarly papers. Training models to extract scientific information from the full texts of scholarly documents is important for improving how we structure and access scientific information. However, there are few annotated corpora that provide full paper texts. This paper presents the NLPSharedTasks corpus, a new resource of 254 full text Shared Task Overview papers in NLP domains with annotated task descriptions. We calculated strict and relaxed inter-annotator agreement scores, achieving Cohen's kappa coefficients of 0.44 and 0.95, respectively. Lastly, we performed a sentence classification task over the dataset, in order to generate a neural baseline for future research and to provide an example of how to preprocess unbalanced datasets of full scientific texts. We achieved an F1 score of 0.75 using SciBERT, fine-tuned and tested on a rebalanced version of the dataset.

NLPSharedTasks: A Corpus of Shared Task Overview Papers in Natural Language Processing Domains

Electronic theses and dissertations (ETDs) contain valuable knowledge that can be useful for a wide range of purposes. To effectively utilize the knowledge contained in ETDs for downstream tasks such as search and retrieval, question-answering, and summarization, the data first needs to be parsed and stored in a format such as XML. However, since most of the ETDs available on the web are PDF documents, parsing them to make their data useful for downstream tasks is a challenge. In this work, we propose a dataset and a framework to help with parsing long scholarly documents such as ETDs. We take the Object Detection approach for document parsing. We first introduce a set of objects that are important elements of an ETD, along with a new dataset ETD-OD that consists of over 25K page images originating from 200 ETDs with bounding boxes around each of the objects. We also propose a framework that utilizes this dataset for converting ETDs to XML, which can further be used for ETD-related downstream tasks. Our code and pre-trained models are available at: https://github.com/Opening-ETDs/ETD-OD.

Parsing Electronic Theses and Dissertations Using Object Detection

The increased interest in time-domain astronomy over the last decades has resulted in a substantial increase in observation report publication leading to a saturation of how astrophysicists read, analyze and classify information. Due to the short life span of the detected astronomical events, information related to the characterization of new phenomena has to be communicated and analyzed very rapidly to allow other observatories to react and conduct their follow-up observations. This paper introduces TDAC: a Time-Domain Astrophysics Corpus. TDAC is the first corpus based on astrophysical observation reports. We also present the NLP experiments we made for named entity recognition based on annotations we made and annotations from the WIESP DEAL shared task.

TDAC, The First Corpus in Time-Domain Astrophysics: Analysis and First Experiments on Named Entity Recognition

Reproducibility is an important feature of science; experiments are retested, and analyses are repeated. Trust in the findings increases when consistent results are achieved. Despite the importance of reproducibility, significant work is often involved in these efforts, and some published findings may not be reproducible due to oversights or errors. In this paper, we examine a myriad of features in scholarly articles published in computer science conferences and journals and test how they correlate with reproducibility. We collected data from three different sources that labeled publications as either reproducible or irreproducible and employed statistical significance tests to identify features of those publications that hold clues about reproducibility. We found the readability of the scholarly article and accessibility of the software artifacts through hyperlinks to be strong signals noticeable amongst reproducible scholarly articles.

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

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