Taiwan

IJCNLP-AACL 2022

 MasakhaNER: Named Entity Recognition Dataset for 20 African languages.

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

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

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). 

SUMEval shared task overview

NTREX-128 -- News Test References for MT Evaluation of 128 Languages

Significant progress has been made on Indonesian NLP. Nevertheless, exploration of the codemixing phenomenon in Indonesian is limited, despite many languages being frequently mixed with Indonesian. In this work, we explore codemixing in Indonesian with four embedded languages, i.e., English, Sundanese, Javanese, and Malay; and introduce IndoRobusta, a framework to evaluate and improve the code-mixing robustness. Our analysis shows that the pretraining corpus bias affects the model’s ability to better handle Indonesian-English codemixing rather than other local languages, despite having higher language diversity.

IndoRobusta: Towards Robustness Against Diverse Code-Mixed Indonesian Local Languages

Amadís de Gaula (AG) and its sequel Sergas de Esplandián (SE) are masterpieces of medieval Spanish chivalric romances. Much debate has been devoted to the role played by their purported author Garci Rodríguez de Montalvo. According to the prologue of AG, which consists of four books, the author allegedly revised the first three books that were in circulation at that time and added the fourth book and SE. However, the extent to which Montalvo edited the materials at hand to compose the extant works has yet to be explored extensively. To address this question, we applied stylometric techniques for the first time. Specifically, we investigated the stylistic differences (if any) between the first three books of AG and his own extensions. Literary style is represented as usage of parts-of-speech n-grams. We performed principal component analysis and k-means to demonstrate that Montalvo's retouching on the first book was minimal, while revising the second and third books in such a way that they came to moderately resemble his authentic creation, that is, the fourth book and SE. Our findings empirically corroborate suppositions formulated from philological viewpoints.

A Stylometric Analysis of Amadís de Gaula and Sergas de Esplandián

This paper is a methodological exploration of the origin of mood in early modern and modern Finnish literary texts using computational methods. We discuss the pre-processing steps as well as the various natural language processing tools used to try to pinpoint where mood can be best detected in text. We also share several tools and resources developed during this process. Our early attempts suggest that overall mood can be computationally detected in the first three paragraphs of a book.

Computational Exploration of the Origin of Mood in Literary Texts

Election speeches play an integral role in communicating the vision and mission of the candidates. From lofty promises to mud-slinging, the electoral candidate accounts for all. However, there remains an open question about what exactly wins over the voters. In this work, we used state-of-the-art natural language processing methods to study the speeches and sentiments of the Republican candidates and Democratic candidates fighting for the 2020 US Presidential election. Comparing the racial dichotomy of the United States, we analyze what led to the victory and defeat of the different candidates. We believe this work will inform the election campaigning strategy and provide a basis for communicating to diverse crowds.

Sentiment is all you need to win US Presidential elections

Use the Metadata, Luke! -- An Experimental Joint Metadata Search and N-gram Trend Viewer for Personal Web Archives

The simple question answering over the knowledge graph concerns answering single-relation questions by querying the facts in the knowledge graph. This task has drawn significant attention in recent years. However, there is a demand for a simple question dataset in the Persian language to study open-domain simple question answering. In this paper, we present the first Persian single-relation question answering dataset and a model that uses a knowledge graph as a source of knowledge to answer questions. We create the ParsSimpleQA dataset semi-automatically in two steps. First, we build single-relation question templates. Next, we automatically create simple questions and answers using templates, entities, and relations from Farsbase. To present the reliability of the presented dataset, we proposed a simple question-answering system that receives questions and uses deep learning and information retrieval techniques for answering questions. The experimental results presented in this paper show that the ParsSimpleQA dataset is very promising for the Persian simple question-answering task.

ParsSimpleQA: The Persian Simple Question Answering Dataset and System over Knowledge Graph

Digital history is the application of computer science techniques to historical data in order to uncover insights into events occurring during specific time periods from the past. This relatively new interdisciplinary field can help identify and record latent information about political, cultural, and economic trends that are not otherwise apparent from traditional historical analysis. This paper presents a method that uses topic modeling and breakpoint detection to observe how extracted topics come in and out of prominence over various time periods. We apply our techniques on British parliamentary speech data from the 19th century. Findings show that some of the events produced are cohesive in topic content (religion, transportation, economics, etc.) and time period (events are focused in the same year or month). Topic content identified should be further analyzed for specific events and undergo external validation to determine the quality and value of the findings to historians specializing in 19th century Britain.

Enhancing Digital History – Event discovery via Topic Modeling and Change Detection

Amis is an endangered language indigenous to Taiwan with limited data available for computational processing. We thus present an Amis-Mandarin dataset containing a parallel corpus of 5,751 Amis and Mandarin sentences and a dictionary of 7,800 Amis words and phrases with their deﬁnitions in Mandarin. Using our dataset, we also established a baseline for machine translation between Amis and Mandarin in both directions. Our dataset can be found at https://github.com/ francisdzheng/amis-mandarin.

MasakhaNER: Named Entity Recognition Dataset for 20 African languages.

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SUMEval shared task overview

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MasakhaNER: Named Entity Recognition Dataset for 20 African languages.

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Next from IJCNLP-AACL 2022

SUMEval shared task overview

Similar lecture

Asking It All: Generating Contextualized Questions for any Semantic Role

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