Malta

Depression has been extensively researched in the NLP field, focusing on individuals&#39; negative emotions. However, people with depression also experience happiness, but this has not been extensively studied. In this work, we conduct a large-scale study of happy moments from social media texts of people with and without depression. We develop an extensive deep learning-based framework to extract happy moments from text in English, and annotate them with topic information, gender labels, and agency and sociality measures. We analyze over 400,000 happy moments and show significant differences in topics, agency, and sociality of depressed and not-depressed users, varying by gender. Our research can help inform psychology interventions aimed at fostering longer-lasting feelings of happiness and represents a promising area of collaboration between computational linguistics and psychology.

EACL 2024

Social Media Portrayals of Happy Moments Among Depressed Individuals

happy moments

depression

mental health

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Depression has been extensively researched in the NLP field, focusing on individuals' negative emotions. However, people with depression also experience happiness, but this has not been extensively studied. In this work, we conduct a large-scale study of happy moments from social media texts of people with and without depression. We develop an extensive deep learning-based framework to extract happy moments from text in English, and annotate them with topic information, gender labels, and agency and sociality measures. We analyze over 400,000 happy moments and show significant differences in topics, agency, and sociality of depressed and not-depressed users, varying by gender. Our research can help inform psychology interventions aimed at fostering longer-lasting feelings of happiness and represents a promising area of collaboration between computational linguistics and psychology.

poster

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
#### [Conference Handbook](https://drive.google.com/file/d/13Wn38q0yev6U3RTgnf4Tn5T9GuAuZ-xW/view?usp=sharing) *downloadable*

To access the EACL 2024 event page you need to register [**here**](https://acl.swoogo.com/EACL2024). 

Please register!

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

While information from the field of linguistic typology has the potential to improve performance on NLP tasks, reliable typological data is a prerequisite. Existing typological databases, including WALS and Grambank, suffer from inconsistencies primarily caused by their categorical format. Furthermore, typological categorisations by definition differ significantly from the continuous nature of phenomena, as found in natural language corpora. In this paper, we introduce a new seed dataset made up of continuous-valued data, rather than categorical data, that can better reflect the variability of language. While this initial dataset focuses on word-order typology, we also present the methodology used to create the dataset, which can be easily adapted to generate data for a broader set of features and languages.

Multilingual Gradient Word-Order Typology from Universal Dependencies

As large-scale language models become the standard for text generation, there is a greater need to tailor the generations to be more or less concise, targeted, and informative, depending on the audience/application. Existing control approaches primarily adjust the semantic (e.g., emotion, topics), structural (e.g., syntax tree, parts-of-speech), and lexical (e.g., keyword/phrase inclusion) properties of text, but are insufficient to accomplish complex objectives such as pacing which control the complexity and readability of the text. In this paper, we introduce CEV-LM - a lightweight, semi-autoregressive language model that utilizes constrained edit vectors to control three complementary metrics (speed, volume, and circuitousness) that quantify the shape of text (e.g., pacing of content). We study an extensive set of state-of-the-art CTG models and find that CEV-LM provides significantly more targeted and precise control of these three metrics while preserving semantic content, using less training data, and containing fewer parameters.

CEV-LM: Controlled Edit Vector Language Model for Shaping Natural Language Generations

Robustness in Natural Language Processing continues to be a pertinent issue, where state of the art models under-perform under naturally shifted distributions. In the context of Question Answering, work on domain adaptation methods continues to be a growing body of research. However, very little attention has been given to the notion of domain generalization under natural distribution shifts, where the target domain is unknown. With drastic improvements in the quality of and access to generative models, we answer the question: How do generated datasets influence the performance of QA models under natural distribution shifts? We perform experiments on 4 different datasets under varying amounts of distribution shift, and analyze how "in-the-wild" generation can help achieve domain generalization. We take a two-step generation approach, generating both contexts and QA pairs to augment existing datasets. Through our experiments, we demonstrate how augmenting reading comprehension datasets with generated data leads to better robustness towards natural distribution shifts.

Generative Data Augmentation using LLMs improves Distributional Robustness in Question Answering

Empathy is crucial in numerous social interactions, including human-robot, patient-doctor, teacher-student, and customer-call centre conversations. Despite its importance, empathy detection in videos continues to be a challenging task because of the subjective nature of empathy and often remains under-explored. Existing studies have relied on scripted or semi-scripted interactions in text-, audio-, or video-only settings that fail to capture the complexities and nuances of real-life interactions. This PhD research aims to fill these gaps by developing a multimodal language model (MMLM) that detects empathy in audiovisual data. In addition to leveraging existing datasets, the proposed study involves collecting real-life interaction video and audio. This study will leverage optimisation techniques like neural architecture search to deliver an optimised small-scale MMLM. Successful implementation of this project has significant implications in enhancing the quality of social interactions as it enables real-time measurement of empathy and thus provides potential avenues for training for better empathy in interactions.

Thesis Proposal: Detecting Empathy Using Multimodal Language Model

Generating multiple translation candidates would enable users to choose the one that satisfies their needs. Although there has been work on diversified generation, there exists room for improving the diversity mainly because the previous methods do not address the overcorrection problem---the model underestimates a prediction that is largely different from the training data, even if that prediction is likely. This paper proposes methods that generate more diverse translations by introducing perturbed k-nearest neighbor machine translation (kNN-MT). Our methods expand the search space of kNN-MT and help incorporate diverse words into candidates by addressing the overcorrection problem. Our experiments show that the proposed methods drastically improve candidate diversity and control the degree of diversity by tuning the perturbation's magnitude.

Generating Diverse Translation with Perturbed kNN-MT

The self-rationalising capabilities of LLMs are appealing because the generated explanations can give insights into the plausibility of the predictions. However, how faithful the explanations are to the predictions is questionable, raising the need to explore the patterns behind them further. To this end, we propose a hypothesis-driven statistical framework. We present how we used a Bayesian network to implement a hypothesis about how a task (in our example, natural language inference) is solved, and its internal states are translated into natural language with templates. Those explanations are then compared to LLM-generated free-text explanations using automatic and human evaluations. This allows us to judge how similar the LLM’s and the Bayesian network’s decision processes are. We present our results and discuss the implications as well as the framework’s potential to approximate LLM decisions better in future work.

A Hypothesis-Driven Framework for the Analysis of Self-Rationalising Models

Large language models (LLMs) have shown remarkable promise in simulating human language use and behavior. In this study, we delve into the intersection of persona variables and the capability of LLMs to simulate different perspectives. We find that persona variables can explain <10\% variance in annotations in existing NLP datasets. Nonetheless, incorporating them via prompting in LLMs provides modest improvement. Persona prompting is most effective on data samples where disagreements among annotators are frequent yet confined to a limited range. A linear correlation exists: the more persona variables influence human response, the better LLMs predictions are using persona prompting. However, when the utility of persona variables is low (i.e., explaining <10\% of human responses), persona prompting has little effect. Most subjective NLP datasets fall into this category, casting doubt on simulating diverse perspectives in the current NLP landscape.

On Sociodemographics Variables and Sociodemographic Prompting

Reasoning over Commonsense Knowledge Bases (CSKB), i.e. CSKB reasoning, has been explored as a way to acquire new commonsense knowledge based on reference knowledge in the original CSKBs and external prior knowledge.
Despite the advancement of Large Language Models (LLM) and prompt engineering techniques in various reasoning tasks, they still struggle to deal with CSKB reasoning.
One of the problems is that it is hard for them to acquire explicit relational constraints in CSKBs from only in-context exemplars, due to a lack of symbolic reasoning capabilities ~\cite{bengio2021deeplearning}.
To this end, we proposed **ConstraintChecker**, a plugin over prompting techniques to provide and check explicit constraints.
When considering a new knowledge instance, ConstraintChecker employs a rule-based module to produce a list of constraints, then it uses a zero-shot learning module to check whether this knowledge instance satisfies all constraints.
The acquired constraint-checking result is then aggregated with the output of the main prompting technique to produce the final output.
Experimental results on CSKB Reasoning benchmarks demonstrate the effectiveness of our method by bringing consistent improvements over all prompting methods.

ConstraintChecker: A Plugin for Large Language Models to Reason on Commonsense Knowledge Bases

The existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment.
In our work, we provide indirect supervision to the navigation agent through a hint generator that provides detailed visual descriptions.
The hint generator assists the navigation agent in developing a global understanding of the visual environment. It directs the agent's attention toward related navigation details, including the relevant sub-instruction, potential challenges in recognition and ambiguities in grounding, and the targeted viewpoint description. 
To train the hint generator, we construct a synthetic dataset based on landmarks in the instructions and visible and distinctive objects in the visual environment.
We evaluate our method on the R2R and R4R datasets and achieve state-of-the-art on several metrics. 
The experimental results demonstrate that generating hints not only enhances the navigation performance but also helps improve the agent's interpretability.

NavHint: Vision and Language Navigation Agent with a Hint Generator

Extracting the attribute value of a product from the given product description is essential for e-commerce functions like product recommendations, search, and information retrieval. Therefore, understanding products in E-commerce. Greater accuracy certainly gives any retailer the edge. The burdensome aspect of this problem lies in the diversity of the products and their attributes and values. Existing solutions typically employ large language models or sequence-tagging approaches to capture the context of a given product description and extract attribute values. However, they do so with limited accuracy, which serves as the underlying motivation to explore a more comprehensive solution. Through this paper, we present a novel approach for attribute value extraction from product description leveraging graphs and graph neural networks. Our proposed method demonstrates improvements in attribute value extraction accuracy compared to the baseline sequence tagging approaches.

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Multilingual Gradient Word-Order Typology from Universal Dependencies

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Social Media Portrayals of Happy Moments Among Depressed Individuals | VIDEO