Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. In this paper, we propose the AMR-TST, an AMR-based text style transfer (TST) technique. The AMR-TST converts the source text to an AMR graph and generates the transferred text based on the AMR graph modified by a TST policy named style rewriting. Our method combines both the explainability and diversity of explicit and implicit TST methods. The experiments show that the proposed method achieves state-of-the-art results compared with other baseline models in automatic and human evaluations. The generated transferred text in qualitative evaluation proves the AMR-TST have significant advantages in keeping semantic features and reducing hallucinations. To the best of our knowledge, this work is the first to apply the AMR method focusing on node-level features to the TST task.

<iframe src="https://app.sli.do/event/8PaBoicsuHK7beXFunvrtr/embed/polls/d5dde25a-f42b-45da-b7b7-d5cff09c8dcf" width="300" height="400"></iframe>

AMR-TST: Abstract Meaning Representation-based Text Style Transfer

Extracting structured and grounded fact triples from raw text is a fundamental task in Information Extraction (IE). Existing IE datasets are typically collected from Wikipedia articles, using hyperlinks to link entities to the Wikidata knowledge base. However, models trained only on Wikipedia have limitations when applied to web domains, which often contain noisy text or text that does not have any factual information. We present WebIE, the first large-scale, entity-linked closed IE dataset consisting of 1.6M sentences automatically collected from the English Common Crawl corpus. WebIE also includes negative examples, i.e. sentences without fact triples, to better reflect the data on the web. We annotate ~25K triples from WebIE through crowdsourcing and introduce mWebIE, a translation of the annotated set in four other languages: French, Spanish, Portuguese, and Hindi. We evaluate the in-domain, out-of-domain, and zero-shot cross-lingual performance of generative IE models and find models trained on WebIE show better generalisability. We also propose three training strategies that use entity linking as an auxiliary task. Our experiments show that adding Entity-Linking objectives improves the faithfulness of our generative IE models.

<iframe src="https://app.sli.do/event/6qSnTPZvioRLfFJpZUjhCw/embed/polls/f51c1033-4ec9-40fd-81f3-473d189153b7" width="300" height="400"></iframe>

WebIE: Faithful and Robust Information Extraction on the Web

One vital job behavior that hospitality organizations should support is helping behavior, as it represents employees’ proactive and reactive responses to the needs of their colleagues (Hwang et al., 2021) and plays a critical role in various service aspects (e.g., Zou et al., 2015; Kim et al., 2018; Özduran and Tanova, 2017). However, there is a paucity of research on how to support employees to engage in helping behavior. Thus, the aim of this study is to increase the understanding of how an employee’s living a calling and cooperative organizational culture impact their workplace well-being, and ultimately their helping behavior toward coworkers. The sample was collected from US hospitality employees using an MTurk panel. Partial Least Squares structural equation modeling and multigroup analysis were used to explain the proposed model. Results revealed that living a calling and cooperative organizational culture positively influenced both workplace well-being and helping behavior, respectively, and workplace well-being positively influenced helping behavior. Additionally, cooperative organizational culture and workplace well-being were significant mediators between living a calling and helping behavior. Cooperative organizational culture was vital for female employees to engage in helping behavior, while the impact of workplace well-being on helping behavior was more important for male employees. This study advances the understanding of employee’s helping behavior by unveiling new motivators (i.e., living a calling, cooperative organization culture, workplace well-being) and extends the understanding of organizational support theory and social identity theory. The study also demonstrates that gender has a significant influence on helping behaviors. Hospitality managers will benefit from this research by understanding the importance of employee’s social identifications and the necessary supportive workplace environment to elevate employee’s well-being and their helping behavior toward coworkers, a critical aspect to increase competitive advantage for organizational success.

The effect of cooperative organizational culture and gender on helping behavior

Virtual Poster Session 3

poster

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
<br>The conference will be held in Toronto, Canada, July 9-14, 2023.<br><br>
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
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so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
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you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience.<br>
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**
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ACL 2023

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.

Poster Session 3


**Session Chair: **Cynthia Mejia<br> 


Breakout Session 5

technical paper



Dear Delegates,<br>  

On behalf of the organizing committee at UCF Rosen College of Hospitality Management and Elsevier, I have the greatest pleasure to welcome you to the **6th World Research Summit for Tourism and Hospitality: Resilience, Adaptability, and Innovation.**<br>  

Though we cannot host you this year owing to Covid-19 at our beautiful resort-like campus in Orlando, Florida, we believe this virtual and live-streamed conference will provide an interactive, dynamic, and shared international platform for global academics and practitioners to discuss how to enhance research applications and knowledge management through resilience, adaptability, digital intelligence, innovation, and smart knowledge in bringing our industry and research back to normal. This summit particularly offers unique opportunities to unfold and embrace various repercussions of COVID-19 pandemic with various stakeholders, including academic researchers, industry professionals, and government officials, through dialogues on how to better utilize research outputs for the benefits of the industry, in a resilient, intelligent and innovative way.<br>    

I hope you will agree that we have put together a wonderful program including keynote speeches, panel discussions, concurrent presentations, poster sessions, and best paper recognition and award ceremony. We hope you will take advantage of these activities and engage yourself with great learning and sharing opportunities during the two days of the Summit. Thank you for joining us and we look forward to our discussions!<br>  

Sincerely,                                                  
Dr. Youcheng Wang<br>
Dean and Preeminent Chair Professor<br>  


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AMR-TST: Abstract Meaning Representation-based Text Style Transfer

WebIE: Faithful and Robust Information Extraction on the Web

The effect of cooperative organizational culture and gender on helping behavior

AMR-TST: Abstract Meaning Representation-based Text Style Transfer

WebIE: Faithful and Robust Information Extraction on the Web

The effect of cooperative organizational culture and gender on helping behavior

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