Taiwan

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-tutorials.2/)


IJCNLP-AACL 2022

Recent Advances in Pre-trained Language Models: Why Do They Work and How Do They Work

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.

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). 
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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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<br>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). 

It has been shown that multilingual transformer models are able to predict human reading behavior when fine-tuned on small amounts of eye tracking data. As the cumulated prediction results do not provide insights into the linguistic cues that the model acquires to predict reading behavior, we conduct a deeper analysis of the predictions from the perspective of readability. We try to disentangle the three-fold relationship between human eye movements, the capability of language models to predict these eye movement patterns, and sentence-level readability measures for English. We compare a range of model configurations to multiple baselines. We show that the models exhibit difficulties with function words and that pre-training only provides limited advantages for linguistic generalization.

Patterns of Text Readability in Human and Predicted Eye Movements.

Inalienable possession differs from alienable possession in that, in the former – e.g., kinships and part-whole relations – there is an intrinsic semantic dependency between the possessor and possessum. This paper reports two studies that used acceptability-judgment tasks to investigate whether native Mandarin speakers experienced different levels of interpretational costs while resolving different types of possessive relations, i.e., inalienable possessions (kinship terms and body parts) and alienable ones, expressed within relative clauses. The results show that sentences received higher acceptability ratings when body parts were the possessum as compared to sentences with alienable possessum, indicating that the inherent semantic dependency facilitates the resolution. However, inalienable kinship terms received the lowest acceptability ratings. We argue that this was because the kinship terms, which had the [+human] feature and appeared at the beginning of the experimental sentences, tended to be interpreted as the subject in shallow processing; these features contradicted the semantic-syntactic requirements of the experimental sentences.

(In)Alienable Possession in Mandarin Relative Clauses

This paper is intended to study the effects of age of acquisition (AoA) and orthographic transparency on word retrieval in Persian, which is an understudied language. A naming task (both pictures and words) and a recall task (both pictures and words) were used to explore how lexical retrieval and verbal memory are affected by AoA and transparency. Seventy two native speakers of Persian were recruited to participate in two experiments. The results showed that early acquired words are processed faster than late acquired words only when pictures were used as stimuli. Transparency of the words was not an influential factor. However, in the recall experiment a three-way interaction was observed: early acquired pictures and words were processed faster than late acquired stimuli except the words in the transparent condition. The findings speak to the fact that language-specific properties of languages are very important.

Do Age of Acquisition and Orthographic Transparency Have the Same Effects in Different Modalities?

Better Smatch = Better Parser? AMR evaluation is not so simple anymore

Evaluating the performance of Massively Multilingual Language Models (MMLMs) is difficult due to the shortage of evaluation datasets in low-resource languages. Due to computational limitations evaluating MMLMs trained on all possible pivot configurations is not feasible. This paper describes our contribution to the SumEval 2022 shared task, which handles the crucial task of Performance prediction of MMLMs. We build upon Microsoft Research's Project LITMUS and devise a method to further improve predictions. We develop various machine-learning approaches which outperform the baseline score provided by LITMUS. Our system ranked first with an RMSE score of 0.017 for the non-surprise and 0.109 for the surprise dataset.

To Train or Not to Train: Predicting the Performance of Massively Multilingual Models

The GMU System Submission for the SUMEval 2022 Shared Task

Phraseology studies have been enhanced by Corpus Linguistics, which has become an interdisciplinary field where current technologies play an important role in its development. Computational tools have been implemented in the last decades with positive results on the identification of phrases in different languages. One specific technology that has impacted these studies is social media. As researchers, we have turned our attention to collecting data from these platforms, which comes with great advantages and its own challenges. One of the challenges is the way we design and build corpora relevant to the questions emerging in this type of language expression. This has been approached from different angles, but one that has given invaluable outputs is the building of linguistic corpora with the use of online web applications. In this paper, we take a multidimensional approach to the collection, design, and deployment of a phraseology corpus for Latin American Spanish from Twitter data, extracting features using NLP techniques, and presenting it in an interactive online web application. We expect to contribute to the methodologies used for Corpus Linguistics in the current technological age. Finally, we make this tool publicly available to be used by any researcher interested in the data itself and also on the technological tools developed here.

Interactive Analysis and Visualisation of Annotated Collocations in Spanish (AVAnCES)

Fractality of sentiment arcs for literary quality assessment: The case of Nobel laureates

In recent years, research on image caption generation has evolved to include not only the generation based on the information obtained from an image, but also the generation based on the user’s interest to image content by providing additional information called control signals. In this study, we propose a method that considers tracing an image as a control signal and reflects the user's intention in tracing in the generation of sentences. The proposed method is characterized by the fact that it not only generates a caption according to the object to be traced, but also considers the amount of time the user has traced a specific object in the image as a control signal that expresses the user's interest on the object. In order to generate sentences whose length is proportional to the tracing time, a non-autoregressive caption generation method is employed. Comparing the proposed method with existing methods for caption generation, the image caption generation that interactively reflects the intent of the explainer using the proposed method successfully generates captions that reflect the order and details of the explanation.

Caption Generation Reflecting the Intent of the Explainer by Tracing on an Image

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-tutorials.6/)


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Patterns of Text Readability in Human and Predicted Eye Movements.

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Next from IJCNLP-AACL 2022

Patterns of Text Readability in Human and Predicted Eye Movements.

Similar lecture

NUT-RC: Noisy User-generated Text-oriented Reading Comprehension

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

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