Taiwan

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


IJCNLP-AACL 2022

Grounding Meaning Representation for Situated Reasoning

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-23 is the Thirty-Seventh AAAI Conference on Artificial Intelligence. The theme of this conference is to create collaborative bridges within and beyond AI. Like previous AAAI conferences, AAAI-23 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and two new activities: a Bridge Program and a Lab Program. Many of these activities are tailored to the theme of bridges and all are selected according to the highest standards, with additional programs for students and young researchers. 
AAAI is providing you with a conference planner, which you can use to help organize your itinerary of activities. This includes talks to attend in person, talks to attend remotely, breaks with colleagues and your site seeing activities. To access this conference planner, please go to [https://aaai-2023.takemobi.io](https://aaai-2023.takemobi.io).

In order to access this site, you need to register. If you haven't already, please register [here](https://aaai.org/Conferences/AAAI-23/registration/).


AAAI 2023

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines.

tutorial

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

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

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


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