Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, challenges remain in handling complex queries that involve multi-table joins, nested conditions, and intricate operations. Existing methods often rely on multi-step pipelines that incur high computational costs, increase latency, and are prone to error propagation. To address these limitations, we propose HI-SQL, a pipeline that incorporates a novel hint generation mechanism utilizing historical query logs to guide SQL generation. By analyzing prior queries, our method generates contextual hints that focus on handling the complexities of multi-table and nested operations. These hints are seamlessly integrated into the SQL generation process, eliminating the need for costly multi-step approaches and reducing reliance on human-crafted prompts. Experimental evaluations on multiple benchmark datasets demonstrate that our approach significantly improves query accuracy of LLM-generated queries while ensuring efficiency in terms of LLM calls and latency, offering a robust and practical solution for enhancing Text-to-SQL systems.

Although there has been a plethora of work on open-domain conversational systems, most of these lack the mechanism of controlling the concept transitions in a dialogue.For activities like switching from casual chit-chat to taskoriented conversation, an agent with the ability to manage the flow of concepts in a conversation might be helpful.The user would find the dialogue more fascinating and engaging and be more receptive to such transitions if these concept transitions were made while taking into account the user's persona.Focusing on personaaware concept transitions, we propose a Reinforced Persona-aware Topic-guiding Conversational System (RPTCS).Due to the lack of a persona-aware topic transition dataset, we propose a novel conversation dataset creation mechanism in which the conversational agent leads the discourse to drift to a set of target concepts depending on the persona of the speaker and the context of the conversation.To avoid scarcely available expensive human resources, the entire data-creation process is mostly automatic with human-in-loop only for quality checks.This created conversational dataset named PTCD is used to develop the RPTCS in two steps.First, a maximum likelihood estimation loss-based dialogue model is trained on PTCD.The trained model is then fine-tuned in a Reinforcement Learning (RL) framework by employing novel reward functions to assure persona, topic, and context consistency with non-repetitiveness in generated responses.Our experimental results demonstrate the strength of the proposed system with respect to strong baselines 1 .

With the growing popularity of code-mixed data, there is an increasing need for better handling of this type of data, which poses a number of challenges, such as dealing with spelling variations, multiple languages, different scripts, and a lack of resources.Current language models face difficulty in effectively handling code-mixed data as they primarily focus on the semantic representation of words and ignore the auditory phonetic features.This leads to difficulties in handling spelling variations in code-mixed text.In this paper, we propose an effective approach for creating language models for handling code-mixed textual data using auditory information of words from SOUNDEX.Our approach includes a pre-training step based on masked-language-modelling, which includes SOUNDEX representations (SAMLM) and a new method of providing input data to the pretrained model.Through experimentation on various code-mixed datasets (of different languages) for sentiment, offensive and aggression classification tasks, we establish that our novel language modeling approach (SAMLM) results in improved robustness towards adversarial attacks on code-mixed classification tasks.Additionally, our SAMLM based approach also results in better classification results over the popular baselines for code-mixed tasks.We use the explainability technique, SHAP (SHapley Additive exPlanations) to explain how the auditory features incorporated through SAMLM assist the model to handle the code-mixed text effectively and increase robustness against adversarial attacks 1 .

Systems meant for tackling hate speech have been increasing in demand with the rapid growth of social media platforms. One way of controlling hate speech in texts is to transform the text into its non-hate version while preserving the rest of the contents. Without the use of parallel data, unsupervised back-translation-based text style transfer is a common method of tackling such problems. In this article, we propose a zero-shot style-transfer technique that does effective unsupervised hate to non-hate conversion without using any hate domain text for training. While decoding the outputs produced by the system, we define an additional step of introducing lexical constraints, for better preservation of contents. Detailed empirical evaluation shows that the zero-shot method outperforms classical unsupervised style-transfer methods while at the same time reducing the data required while training.

In this paper, we propose an unsupervised approach for knowledge graph (KG) creation from conversational data. We make use of intent classification and slot-filling, the two important components of any dialogue agent, exploit their interconnectedness, and finally construct a KG. We build a supervised intent classifier to extract the intent classes, and then on top of this we run our occlusion based slot-information extraction algorithm. Our algorithm is able to make use of supervised training of intent classifiers for extracting the relevant slot-information in an unsupervised way. To test the effectiveness of our system, we perform both automatic and manual evaluation of our intent-classifier and slot-filling system on three dialog datasets. Finally, we construct a knowledge graph from the dialogue conversation using an algorithm that makes use of our occlusion based slot-information extraction module. Empirical evaluation shows that our occlusion based method is able to successfully extract slot information from conversations, resulting in a high-quality KG.

Emotion analysis from texts has emerged as an important area of research in the field of Natural Language Processing (NLP) in the past few years. Several benchmark datasets have been released for this task, however most of the datasets are open domain in nature and for resource-rich language like English. These datasets may not always be sufficient for capturing domain specific emotions, and may tend to skew towards an emotion based on the domain. In this paper, we provide a framework for multilingual emotion detection to deal with the crisis situations. We collect and annotate disaster domain social media tweets and news data in two languages, namely English and Hindi. We derive 6 emotions from Plutchik's wheel of emotions suitable for disaster domain, and annotate the data using these disaster specific emotions. In total we create four emotionally enriched datasets i.e. 2 tweets datasets (English and Hindi) and 2 news dataset (English and Hindi). We also establish strong baselines on the dataset using two popular deep learning algorithms, stacked Bi-LSTM+CNN and BERT. Evaluation shows that the best model achieves the averaged accuracy of 68% across the four different datasets.

Text generator systems have become extremely popular with the advent of recent deep learning models such as encoder-decoder. Controlling the information and style of the generated output without supervision is an important and challenging Natural Language Processing (NLP) task. In this paper we define the task of constructing a coherent paragraph from a set of disaster domain tweets, without any parallel data. We tackle the problem by building two systems in pipeline. The first system focuses on unsupervised style transfer to convert the individual tweets into news sentences. The second system stitches together the outputs from the first system to form a coherent news paragraph. We also propose a novel training mechanism, by splitting the sentences into propositions and training the second system to merge the sentences. We create a validation and test set consisting of tweet-sets and their equivalent news paragraphs to perform empirical evaluation. We also perform human evaluations on our model. In a completely unsupervised setting our model was able to achieve a BLEU score of 19.32, while successfully transferring styles and joining tweets to form a meaningful news paragraph.