In medical dialogue systems, recent advancements underscore the critical role of incorporating relevant medical knowledge to enhance performance. However, existing knowledge bases often lack completeness, posing a challenge in sourcing pertinent information. We present MedProm, a novel generative model tailored for medical dialogue generation to address this gap. Motivated by the need for comprehensive and contextually relevant responses, MedProm leverages state-of-the-art language models such as BioGPT. Our model is designed to integrate extensive medical knowledge into conversations, facilitating effective communication between patients and healthcare providers. At the core of MedProm lies the MediConnect Graph, a meticulously constructed knowledge graph capturing intricate relationships among medical entities extracted from dialogue contexts. By employing a KnowFusion encoder with a pretraining objective and masked multi-head self-attention, MedProm effectively processes the MediConnect graph, enabling precise control over information flow to capture its underlying structure. Furthermore, MedProm incorporates a sophisticated Curriculum Knowledge Decoder, leveraging transformer-based decoding to generate response utterances conditioned on input representations from the KnowFusion Encoder. The training process is guided through curriculum learning, gradually increasing optimization difficulty based on a coherence-based criterion. Experimental results on two datasets demonstrate the efficacy of MedProm in generating accurate and contextually relevant responses compared to state-of-the-art models.

Sustainability reports are key for evaluating companies' environmental, social and governance (ESG) performance.To analyze these reports, NLP approaches can efficiently extract ESG insights at scale.However, even the most advanced NLP methods lack robustness against ESG content that is greenwashed -i.e.sustainability claims that are misleading, exaggerated, and fabricated.Accordingly, existing NLP approaches often extract insights that reflect misleading or exaggerated sustainability claims rather than objective ESG performance.To tackle this issue, we introduce A3CG -Aspect-Action Analysis with Cross-Category Generalization, as a novel dataset to improve the robustness of ESG analysis amid the prevalence of greenwashing.By explicitly linking sustainability aspects with their associated actions, A3CG facilitates a more fine-grained and transparent evaluation of sustainability claims, ensuring that insights are grounded in verifiable actions rather than vague or misleading rhetoric.Additionally, A3CG emphasizes cross-category generalization.This ensures robust model performance in aspectaction analysis even when companies change their reports to selectively favor certain sustainability areas.Through experiments on A3CG, we analyze state-of-the-art supervised models and LLMs, uncovering their limitations and outlining key directions for future research.

Counterfactual reasoning typically involves considering alternatives to actual events.While often applied to understand past events, a distinct form-forward counterfactual reasoningfocuses on anticipating plausible future developments.This type of reasoning is invaluable in dynamic financial markets, where anticipating market developments can powerfully unveil potential risks and opportunities for stakeholders, guiding their decision-making.However, performing this at scale is challenging due to the cognitive demands involved, underscoring the need for automated solutions.LLMs offer promise, but remain unexplored for this application.To address this gap, we introduce a novel benchmark, FIN-FORCE-FINancial FORward Counterfactual Evaluation.By curating financial news headlines and providing structured evaluation, FIN-FORCE supports LLM based forward counterfactual generation.This paves the way for scalable and automated solutions for exploring and anticipating future market developments, thereby providing structured insights for decision-making.Through experiments on FIN-FORCE, we evaluate state-of-theart LLMs and counterfactual generation methods, analyzing their limitations and proposing insights for future research.We release the benchmark, supplementary data and all experimental codes at the following link

The development of conversational agents to interact with patients and deliver clinical advice has attracted the interest of many researchers, particularly in light of the COVID-19 pandemic.The training of an end-to-end neural based dialog system, on the other hand, is hampered by a lack of multi-turn medical dialog corpus.We make the very first attempt to release a highquality multi-turn Medical Dialog dataset relating to Covid-19 disease named CDialog, with over 1K conversations collected from the online medical counselling websites.We annotate each utterance of the conversation with seven different categories of medical entities, including diseases, symptoms, medical tests, medical history, remedies, medications and other aspects as additional labels.Finally, we propose a novel neural medical dialog system based on the CDialog dataset to advance future research on developing automated medical dialog systems.We use pre-trained language models for dialogue generation, incorporating annotated medical entities, to generate a virtual doctor's response that addresses the patient's query.Experimental results show that the proposed dialog models perform comparably better when supplemented with entity information and hence can improve the response quality.

Grounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-references is critical for dialogue comprehension and generation.In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based knowledge in addition to the unstructured topic-specific knowledge associated with each utterance.We enhance the commonsense knowledge with named entity-aware structures using co-references.Our proposed model utilizes a multi-hop attention layer to preserve the most accurate and critical parts of the dialogue history and the associated knowledge.In addition, we employ a Commonsense and Named Entity Enhanced Attention Module, which starts with the extracted triples from various sources and gradually finds the relevant supporting set of triples using multi-hop attention with the query vector obtained from the interactive dialogue-knowledge module.Empirical results on two benchmark dataset demonstrate that our model significantly outperforms the state-of-the-art methods in terms of both automatic evaluation metrics and human judgment.Our code is publicly available at https://github.com/deekshaVarshney/CNTF;https://www.iitp.ac.in/-ai-nlp-ml/resources/ codes/CNTF.zip.

A recent topic of research in natural language generation has been the development of automatic response generation modules that can automatically respond to a user's utterance in an empathetic manner.Previous research has tackled this task using neural generative methods by augmenting emotion classes with the input sequences.However, the outputs by these models may be inconsistent.We employ multitask learning to predict the emotion label and to generate a viable response for a given utterance using a common encoder with multiple decoders.Our proposed encoder-decoder model consists of a self-attention based encoder and a decoder with dot product attention mechanism to generate response with a specified emotion.We use the focal loss to handle imbalanced data distribution, and utilize the consistency loss to allow coherent decoding by the decoders.Human evaluation reveals that our model produces more emotionally pertinent responses.In addition, our model outperforms multiple strong baselines on automatic evaluation measures such as F1 and BLEU scores, thus resulting in more fluent and adequate responses.

Emotion Recognition in Conversation (ERC) is becoming increasingly popular due to the accessibility of an enormous measure of openly accessible conversational information. Moreover, it has potential applications in opinion mining, social media and the health care domain. In this paper, we propose a novel Context and Knowledge Enriched Transformer Framework (CKETF) in which we interpret the contextual information from the utterances using a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model and leverage additive attention based hierarchical transformer for encoding the knowledge sentences. Experiments on the knowledge-grounded Topical Chat dataset shows that both context and external knowledge are important for conversational emotion recognition. We demonstrate through extensive experiments and analysis that our proposed model significantly outperforms the current state-of-the-art methods.