Taiwan

Recent studies have reported that neural reasoning improves its accuracy when generating the intermediate reasoning steps. However, it is yet unclear what factors lead to such benefits.
We investigate and factorize the benefit of generating intermediate steps for symbolic reasoning.
Specifically, we decomposed the reasoning strategy regarding step granularity and chaining strategy. 
With a purely symbolic numerical reasoning dataset (e.g., A=1, B=3, C=A+3, C?), we found that the choice of reasoning strategies significantly affects the performance, with the gap becoming even larger as the extrapolation length becomes longer.
Surprisingly, we also found that certain configurations lead to nearly perfect performance, even in the case of length extrapolation.
Our results indicate the importance of further exploring effective strategies for neural reasoning models.

IJCNLP-AACL 2022

Empirical Investigation of Neural Symbolic Reasoning Strategies

multi-hop arithmetic reasoning

neuro-symbolic reasoning

explainability

# 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). 

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

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.

Compositionality as an Analogical Process: Introducing ANNE.


Random Text Perturbations Work, but not Always

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.

Empirical Investigation of Neural Symbolic Reasoning Strategies

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Empirical Investigation of Neural Symbolic Reasoning Strategies

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