Peer review is a cornerstone of quality control in scientific publishing.With the increasing workload, the unintended use of 'quick' heuristics, referred to as lazy thinking, has emerged as a recurring issue compromising review quality.Automated methods to detect such heuristics can help improve the peer-reviewing process.However, there is limited NLP research on this issue, and no real-world dataset exists to support the development of detection tools.This work introduces LAZYREVIEW, a dataset of peer-review sentences annotated with finegrained lazy thinking categories.Our analysis reveals that Large Language Models (LLMs) struggle to detect these instances in a zeroshot setting.However, instruction-based finetuning on our dataset significantly boosts performance by 10-20 performance points, highlighting the importance of high-quality training data.Furthermore, a controlled experiment demonstrates that reviews revised with lazy thinking feedback are more comprehensive and actionable than those written without such feedback.We will release our dataset and the enhanced guidelines that can be used to train junior reviewers in the community.1 Heuristics Description Example review segmentsThe results are not surprising Many findings seem obvious in retrospect, but this does not mean that the community is already aware of them and can use them as building blocks for future work.

Visual Question Answering~(VQA) is an important task in multimodal AI, which requires models to understand and reason on knowledge present in visual and textual data. However, most of the current VQA datasets and models are primarily focused on English and a few major world languages, with images that are Western-centric. While recent efforts have tried to increase the number of languages covered on VQA datasets, they still lack diversity in low-resource languages. More importantly, some datasets extend the text to other languages, either via translation or some other approaches, but usually keep the same images, resulting in narrow cultural representation. To address these limitations, we create CVQA, a new Culturally-diverse Multilingual Visual Question Answering benchmark dataset, designed to cover a rich set of languages and regions, where we engage native speakers and cultural experts in the data collection process. CVQA includes culturally-driven images and questions from across 28 countries in four continents, covering 26 languages with 11 scripts, providing a total of 9k questions. We benchmark several Multimodal Large Language Models (MLLMs) on CVQA, and we show that the dataset is challenging for the current state-of-the-art models. This benchmark will serve as a probing evaluation suite for assessing the cultural bias of multimodal models and hopefully encourage more research efforts towards increasing cultural awareness and linguistic diversity in this field.

In many domains of argumentation, people's arguments are driven by so-called attitude roots, i.e., underlying beliefs and world views, and their corresponding attitude themes.Given the strength of these latent drivers of arguments, recent work in psychology suggests that instead of directly countering surface-level reasoning (e.g., falsifying given premises), one should follow an argumentation style inspired by the Jiu-Jitsu "soft" combat system (Hornsey and Fielding, 2017): first, identify an arguer's attitude roots and themes, and then choose a prototypical rebuttal that is aligned with those drivers instead of invalidating those.In this work, we are the first to explore Jiu-Jitsu argumentation for peer review by proposing the novel task of attitude and theme-guided rebuttal generation.To this end, we enrich an existing dataset for discourse structure in peer reviews with attitude roots, attitude themes, and canonical rebuttals.To facilitate this process, we recast established annotation concepts from the domain of peer reviews (e.g., aspects a review sentence is relating to) and train domain-specific models.We then propose strong rebuttal generation strategies, which we benchmark on our novel dataset for the task of end-to-end attitude and themeguided rebuttal generation and two subtasks.1

Knowledge Graph Question Answering (KGQA) has become a prominent area in natural language processing due to the emergence of large scale Knowledge Graphs (KGs). Semantic parsing based approach is the predominant direction to solve the KGQA task where natural language question is translated into a logic form such as SPARQL query. Recently Neural Machine Translation (NMT) based approaches are gaining momentum in order to translate natural language query to structured query languages thereby solving the KGQA task. However, most of these methods struggle with out-of-vocabulary words where test entities and relations are not seen during training time. In this work, we propose a modular two stage neural architecture to solve the KGQA task. Stage-I of our approach comprises a NMT-based seq2seq module that translates a question into a sketch of the desired SPARQL query called a SPARQL silhouette. Stage-II of our approach comprises a Neural Graph Search (NGS) module which aims to improve the quality of the SPARQL silhouette by detecting the right relations in the underlying knowledge graph. Experimental results show that we achieve substantial improvements and obtain state-of-the-art performance or comparable results to the best performing systems on two benchmark datasets. We believe, our proposed approach is novel and will lead to dynamic KGQA solutions that are well-suited for practical applications.

Identification of potential Drug-Drug Interactions (DDI) for newly developed drugs is essential in public healthcare. Computational methods of DDI prediction rely on known interactions to learn possible interaction between drug pairs whose interactions are unknown. Past work has used various similarity measures of drugs to predict DDIs. In this paper, we propose an effective approach to DDI Prediction using rich drug representations utilizing multiple knowledge sources. We have used the Drug-Target Interaction (DTI) Network to learn an embedding of drugs by using the metapath2vec algorithm. We have also used drug representation gained from the rich chemical structure representation of drugs using Variational Auto-Encoder. The DDI prediction problem is modeled as a link prediction problem in the DDI network containing known interactions. We represent the nodes in the DDI network as their embeddings. We apply a link prediction algorithm based on Graph Auto-Encoders to predict additional edges in this network, which are potential interactions. We have evaluated our approach on three benchmark DDI datasets, namely DrugBank, SemMedDB, and BioSNAP. Experimental results demonstrate that the proposed method outperforms the prior methods in terms of several performance metrics (AUC, AUPR, and F1-score) on all the datasets. Furthermore, we have also evaluated the role of the individual type of drug representation embeddings in boosting up the performance of DDI Prediction.