Taiwan

Cross-lingual transfer learning has proven useful in a variety of Natural Language Processing (NLP) tasks, but it is understudied in the context of legal NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer learning techniques on LJP using the trilingual Swiss-Judgment-Prediction dataset, including cases written in three languages. We find that cross-lingual transfer improves the overall results across languages, especially when we use adapter-based fine-tuning. Finally, we further improve the model&#39;s performance by augmenting the training dataset with machine-translated versions of the original documents, using a 3x larger training corpus. Further on, we perform an analysis exploring the effect of cross-domain and cross-regional transfer, i.e., train a model across domains (legal areas), or regions. We find that in both settings (legal areas, origin regions), models trained across all groups perform overall better, while they also have improved results in the worst-case scenarios. Finally, we report improved results when we ambitiously apply cross-jurisdiction transfer, where we further augment our dataset with Indian legal cases.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.3/)

IJCNLP-AACL 2022

An Empirical Study on Cross-X Transfer for Legal Judgment Prediction

switzerland

judgment prediction

india

french

english

crosslingual transfer

adapters

italian

german

multilingual

# 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). 

Cross-lingual transfer learning has proven useful in a variety of Natural Language Processing (NLP) tasks, but it is understudied in the context of legal NLP, and not at all in Legal Judgment Prediction (LJP). We explore transfer learning techniques on LJP using the trilingual Swiss-Judgment-Prediction dataset, including cases written in three languages. We find that cross-lingual transfer improves the overall results across languages, especially when we use adapter-based fine-tuning. Finally, we further improve the model's performance by augmenting the training dataset with machine-translated versions of the original documents, using a 3x larger training corpus. Further on, we perform an analysis exploring the effect of cross-domain and cross-regional transfer, i.e., train a model across domains (legal areas), or regions. We find that in both settings (legal areas, origin regions), models trained across all groups perform overall better, while they also have improved results in the worst-case scenarios. Finally, we report improved results when we ambitiously apply cross-jurisdiction transfer, where we further augment our dataset with Indian legal cases.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.3/)

technical 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). 

BERT-like language models (LMs), when exposed to large unstructured datasets, are known to learn and sometimes even amplify the biases present in such data. These biases generally reflect social stereotypes with respect to gender, race, age, and others. In this paper, we analyze the variations in gender and racial biases in BERT, a large pre-trained LM, when exposed to different demographic groups. Specifically, we investigate the effect of fine-tuning BERT on text authored by historically disadvantaged demographic groups in comparison to that by advantaged groups. We show that simply by fine-tuning BERT-like LMs on text authored by certain demographic groups can result in the mitigation of social biases in these LMs against various target groups.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.38/)

Demographic-Aware Language Model Fine-tuning as a Bias Mitigation Technique

Conversational AI systems can engage in unsafe behaviour when handling users' medical queries that may have severe consequences and could even lead to deaths.
Systems therefore need to be capable of both recognising the seriousness of medical inputs and producing responses with appropriate levels of risk. 
We create a corpus of human written English language medical queries and the responses of different types of systems. We label these with both crowdsourced and expert annotations.
While individual crowdworkers may be unreliable at grading the seriousness of the prompts, their aggregated labels tend to agree with professional opinion to a greater extent on identifying the medical queries and recognising the risk types posed by the responses.Results of classification experiments suggest that, while these tasks can be automated, caution should be exercised, as errors can potentially be very serious.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.30/)

Risk-graded Safety for Handling Medical Queries in Conversational AI

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.44/)

Modeling Document-level Temporal Structures for Building Temporal Dependency Graph

The use of deep neural models for diagnosis prediction from clinical text has shown promising results. However, in clinical practice such models must not only be accurate, but provide doctors with interpretable and helpful results. We introduce ProtoPatient, a novel method based on prototypical networks and label-wise attention with both of these abilities. ProtoPatient makes predictions based on parts of the text that are similar to prototypical patients–providing justifications that doctors understand. We evaluate the model on two publicly available clinical datasets and show that it outperforms existing baselines. Quantitative and qualitative evaluations with medical doctors further demonstrate that the model provides valuable explanations for clinical decision support.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.14/)

This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text

While there has been substantial progress in developing systems to automate fact-checking, they still lack credibility in the eyes of the users. Thus, an interesting approach has emerged: to perform automatic fact-checking by verifying whether an input claim has been previously fact-checked by professional fact-checkers and to return back an article that explains their decision. This is a sensible approach as people trust manual fact-checking, and as many claims are repeated multiple times. Yet, a major issue when building such systems is the small number of known tweet--verifying article pairs available for training. Here, we aim to bridge this gap by making use of crowd fact-checking, i.e., mining claims in social media for which users have responded with a link to a fact-checking article. In particular, we mine a large-scale collection of 330,000 tweets paired with a corresponding fact-checking article. We further propose an end-to-end framework to learn from this noisy data based on modified self-adaptive training, in a distant supervision scenario. Our experiments on the CLEF'21 CheckThat! test set show improvements over the state of the art by two points absolute. Our code and datasets are available at https://github.com/mhardalov/
crowdchecked-claims.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.22/)

CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media

Automated reporting has the potential to assist radiologists with the time-consuming procedure of generating text radiology reports. Most existing approaches generate the report directly from the radiology image, however we observe that the resulting reports exhibit a realistic style but lack clinical accuracy. Therefore, we propose a two-step pipeline that subdivides the problem into factual triple extraction followed by free-text report generation. The first step comprises supervised extraction of clinically relevant structured information from the image, expressed as triples of the form (entity1, relation, entity2). In the second step, these triples are input to condition the generation of the radiology report. In particular, we focus our work on Chest X-Ray (CXR) radiology report generation. The proposed framework shows state-of-the-art results on the MIMIC-CXR dataset according to most of the standard text generation metrics that we employ (BLEU, METEOR, ROUGE) and to clinical accuracy metrics (recall, precision and F1 assessed using the CheXpert labeller), also giving a 23% reduction in the total number of errors and a 29% reduction in critical clinical errors as assessed by expert human evaluation. In future, this solution can easily integrate more advanced model architectures – to both improve the triple extraction and the report generation – and can be applied to other complex image captioning tasks, such as those found in the medical domain.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.47/)

Multimodal Generation of Radiology Reports using Knowledge-Grounded Extraction of Entities and Relations

Universal Sentence Encoder (USE) has gained much popularity recently as a general-purpose sentence encoding technique. As the name suggests, USE is designed to be fairly general and has indeed been shown to achieve superior performances for many downstream NLP tasks. In this paper, we present an interesting "negative'' result on USE in the context of zero-shot text classification, a challenging task, which has recently gained much attraction. More specifically, we found some interesting cases of zero-shot classification, where topic based inference outperformed USE-based inference in terms of F1 score. Further investigation revealed that USE struggles to perform well on datasets with a large number of labels with high semantic overlaps, while topic-based classification works well for the same.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.18/)

Exploring Universal Sentence Encoders for Zero-shot Text Classification

We address the challenging task of Localization via Embodied Dialog (LED). Given a dialog from two agents, an Observer navigating through an unknown environment and a Locator who is attempting to identify the Observer's location, the goal is to predict the Observer's final location in a map of the environment. We present the novel LED-Bert architecture and demonstrate an effective pre-training strategy from disembodied web data which allows our method to achieve state of the art performance on the task. Additionally during experimentation we find that using a graph-based map representation is more effective than the top-down 2D maps used in prior works.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.36/)

Transformer-based Localization from Embodied Dialog with Large-scale Pre-training

Topic-sensitive query set expansion is an important area of research that aims to improve search results for information retrieval. It is particularly crucial for queries related to sensitive and emerging topics. In this work, we describe a method for query set expansion about emerging topics using vector space interpolation. We use a transformer model called OPTIMUS, which is suitable for vector space manipulation due to its variational autoencoder nature. One of our proposed methods -- Dirichlet interpolation shows promising results for query expansion. Our methods effectively generate new queries about the sensitive topic by incorporating set-level diversity, which is not captured by traditional sentence-level augmentation methods such as paraphrasing or back-translation.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.50/)

Vector Space Interpolation for Query Expansion

In this talk, I will share my perspective on making conversational AI more inclusive and accessible through advances in natural language understanding, speech recognition, and by incorporating interaction affordances that make conversational AI more useful for everyone. I will start with a brief overview of accessibility considerations and some Alexa-powered user experiences that address those considerations. That overview will be followed by a description of some of our recent and ongoing AI/ML work in natural language understanding (Alexa Teacher Model) and in ASR with a focus on achieving fairness at scale. I will conclude with a brief mention of our engagements with academia, including through the Alexa Prize and through the release of publicly available datasets and associated research tasks.

An Empirical Study on Cross-X Transfer for Legal Judgment Prediction

Downloads

Next from IJCNLP-AACL 2022

Demographic-Aware Language Model Fine-tuning as a Bias Mitigation Technique

Similar lecture

xGQA: Cross-Lingual Visual Question Answering

An Empirical Study on Cross-X Transfer for Legal Judgment Prediction

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from IJCNLP-AACL 2022

Demographic-Aware Language Model Fine-tuning as a Bias Mitigation Technique

Similar lecture

xGQA: Cross-Lingual Visual Question Answering

Downloads