Taiwan

In this presentation, we introduce a new approach that simulates the construction of phrasal meaning as an analogical process implemented with deep learning techniques. We propose a distributional representation of constructional phrases (namely Relational Embeddings) and a neural network model for generating new embeddings analogically rather than applying traditional compositional operations, which we call ANNE (Analogical Neural Network for Embeddings). 
We experimented with our analogical neural network to understand how it can generalize and be extendable to different scenarios. We argued that the proposed methodology could open the doors to new analyses in distributional semantics as well as in computational models of language processing.

IJCNLP-AACL 2022

Compositionality as an Analogical Process: Introducing ANNE.


analogy generation

phrasal embeddings

construction grammar

distributional semantics

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

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). 
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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:
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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 present a publicly available corpus with detailed annotations describing the core elements of clinical trials: Participants, Intervention, Control, and Outcomes. The corpus consists of 1011 abstracts of breast cancer randomized controlled trials extracted from the PubMed database. The corpus improves previous corpora by providing detailed annotations for outcomes to identify numeric texts that report the number of participants that experience specific outcomes. The corpus will be helpful for the development of systems for automatic extraction of data from randomized controlled trial literature to support evidence-based medicine. Additionally, we demonstrate the feasibility of the corpus by using two strong baselines for named entity recognition task. Most of the entities achieve F1 scores greater than 0.80 demonstrating the quality of the dataset.

PICO Corpus: A Publicly Available Corpus to Support Automatic Data Extraction from Biomedical Literature

arXiv and Information Extraction

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

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

A Comparative Analysis of Stance Detection Approaches and Datasets

Assessing Neural Referential Form Selectors on a Realistic Multilingual Dataset

Assessing Resource-Performance Trade-off of Natural Language Models using Data Envelopment Analysis

This corpus contains more than 30,000 classical Chinese poems in their tokenized form. The poems are from the Complete Tang Poems (全唐詩,CTP) and the Complete Song Poems (全宋詩,CSP). Two groups of annotators from two universities carried out the task. More than 5000 of these poems were annotated repeatedly for the inter-rater agreement analysis. The annotators did not agree with each other perfectly, as many readers may have expected, but the degrees and types of their agreements are worthy of discussion. The annotations support some comments, of the Ming dynasty, about the formats of the poems. We applied machine learning methods to train and test their usability for the tokenization task. We could identify 87% of the words, 77% of the whole lines, and 18% of the whole poems in the CTP correctly. We have expanded the scale of our annotated poems, have extended our exploration with deep learning methods, and we wish to report these extensions in a formal report.

Introducing a Large Corpus of Tokenized Classical Chinese Poems of Tang and Song Dynasties

Research has shown that the practice of translation exhibits predictable linguistic cues that make translated texts distinguishable from original-language texts (a phenomenon known as "translationese"). In this paper, we test the extent to which literary translations are subject to the same effects and whether they also exhibit meaningful differences at the level of content. Research into the function of translations within national literary markets using smaller case studies has suggested that translations play a cultural role that is distinct from that of original-language literature, i.e. their differences reside not only at the level of translationese but at the level of content. Using a dataset consisting of original-language fiction in English and translations into English from 120 languages (N=21,302), we find that one of the principal functions of literary translation is to convey predictable geographic identities to local readers that nevertheless extend well beyond the foreignness of persons and places.

The predictability of literary translation

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Compositionality as an Analogical Process: Introducing ANNE.

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