Medical images are widely used in clinical decision-making, where writing radiology reports is a potential application that can be enhanced by automatic solutions to alleviate physicians' workload. In general, radiology report generation is an image-text task, where cross-modal mappings between images and texts play an important role in generating high-quality reports. Although previous studies attempt to facilitate the alignment via the co-attention mechanism under supervised settings, they suffer from lacking valid and accurate correspondences due to no annotation of such alignment. In this paper, we propose an approach with reinforcement learning (RL) over a cross-modal memory (CMM) to better align visual and textual features for radiology report generation. In detail, a shared memory is used to record the mappings between visual and textual information, and the proposed reinforced algorithm is performed to learn the signal from the reports to guide the cross-modal alignment even though such reports are not directly related to how images and texts are mapped. Experimental results on two English radiology report datasets, i.e., IU X-Ray and MIMIC-CXR, show the effectiveness of our approach, where the state-of-the-art results are achieved. We further conduct human evaluation and case study which confirm the validity of the reinforced algorithm in our approach.

Reinforced Cross-modal Alignment for Radiology Report Generation

Most recent studies for relation extraction (RE) leverage the dependency tree of the input sentence to incorporate syntax-driven contextual information to improve model performance, with little attention paid to the limitation where high-quality dependency parsers in most cases unavailable, especially for in-domain scenarios. To address this limitation, in this paper, we propose attentive graph convolutional networks (A-GCN) to improve neural RE methods in an unsupervised manner to build the context graph, without relying on the existence of a dependency parser. Specifically, we construct the graph from n-grams extracted from a lexicon built from pointwise mutual information (PMI) and apply attention to the graph. Therefore, different word pairs from the contexts within and across n-grams are weighted in the model and facilitate RE accordingly. Experimental results with further analyses on two English benchmark datasets for RE demonstrate the effectiveness of our approach, where state-of-the-art performance is observed on both datasets.

Relation Extraction with Word Graphs from N-grams

Aspect-based sentiment analysis (ABSA) predicts the sentiment polarity towards a particular aspect term in an input sentence, which is significantly important in real-world applications. To perform ABSA, one faces a long-standing challenge that ABSA requires the model to have a good understanding of the contextual information, especially the particular patterns that suggests the sentiment polarity. However, these patterns vary in different sentences, especially when the sentences come from different sources (domains). Although leveraging extra labeled data from different sources (domains) is always a promising solution to address the challenge, in practical applications, these labeled data are always stored at different locations and might be inaccessible to each other due to privacy or legal concerns (e.g., the data are owned by different companies). To address the issue and benefit from extra labeled data, we propose a novel ABSA model with federated learning (FL) adopted to overcome the data isolation limitations and topic memory (TM) proposed to enhance FL by accommodating the cases of data from diverse sources (domains). Particularly, TM contributes to overcoming difficulties in identifying domains due to data inaccessibility by providing useful categorical information for localized predictions. Experimental results on a simulated environment for FL with three nodes demonstrate the effectiveness of our approach, where TM-FL outperforms different baselines including some well-designed FL frameworks.

Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories

Posters: NLP Applications

poster

# 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) 
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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). 

Virtual Poster Session I: Information Retrieval and Text Mining

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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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.

Han Qin

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SHORT BIO

Presentations

Reinforced Cross-modal Alignment for Radiology Report Generation

Relation Extraction with Word Graphs from N-grams

Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories

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