Taiwan

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.

IJCNLP-AACL 2022

Sentiment is all you need to win US Presidential elections

us elections

speech

sentiment analysis

# 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). 

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.

A Parallel Corpus and Dictionary for Amis-Mandarin Translation

The industrialization process associated with the so-called Industrial Revolution in 19th-century Great Britain was a time of profound changes, including in the English lexicon. An important yet understudied phenomenon is the semantic shift in the lexicon of mechanisation. In this paper we present the first large-scale analysis of terms related to mechanization over the course of the 19th century in English. We draw on a corpus of historical British newspapers comprising 4.6 billion tokens and train historical word embedding models. We test existing semantic change detection techniques and analyse the results in light of previous historical linguistic scholarship.

Machines in the media: semantic change in the lexicon of mechanization in 19th-century British newspapers

Optimizing the weighted sequence alignment algorithm for large-scale text similarity computation

We perform a direct intrinsic evaluation of word embeddings trained on the works of a single philosopher. Six models are compared to human judgements elicited using two tasks: a synonym detection task and a coherence task. We apply a method that elicits judgements based on explicit knowledge from experts, as the linguistic intuition of non-expert participants might differ from that of the philosopher. We find that an in-domain SVD model has the best 1-nearest neighbours for target terms, while transfer learning-based Nonce2Vec performs better for low frequency target terms.

Domain-specific Evaluation of Word Embeddings for Philosophical Text using Direct Intrinsic Evaluation

We describe initial work in developing a methodology for the automatic generation of a conversational agent or ‘chatbot’ through term and relation extraction from a relevant corpus of language data. We develop our approach in the domain of industrial heritage in the 18th and 19th centuries, and more specifically on the industrial history of canals and mills in Ireland. We collected a corpus of relevant newspaper reports and Wikipedia articles, which we deemed representative of a layman’s understanding of this topic. We used the Saffron toolkit to extract relevant terms and relations between the terms from the corpus and leveraged the extracted knowledge to query the British Library Digital Collection and the Project Gutenberg library. We leveraged the extracted terms and relations in identifying possible answers for a constructed set of questions based on the extracted terms, by matching them with sentences in the British Library Digital Collection and the Project Gutenberg library. In a final step, we then took this data set of question-answer pairs to train a chatbot. We evaluate our approach by manually assessing the appropriateness of the generated answers for a random sample, each of which is judged by four annotators.

Sentiment is all you need to win US Presidential elections

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