Rajdeep Sarkar


KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings
Rajdeep Sarkar | Mihael Arcan | John McCrae
Proceedings of the 4th Workshop on NLP for Conversational AI

Knowledge-grounded dialogue systems utilise external knowledge such as knowledge graphs to generate informative and appropriate responses. A crucial challenge of such systems is to select facts from a knowledge graph pertinent to the dialogue context for response generation. This fact selection can be formulated as path traversal over a knowledge graph conditioned on the dialogue context. Such paths can originate from facts mentioned in the dialogue history and terminate at the facts to be mentioned in the response. These walks, in turn, provide an explanation of the flow of the conversation. This work proposes KG-CRuSE, a simple, yet effective LSTM based decoder that utilises the semantic information in the dialogue history and the knowledge graph elements to generate such paths for effective conversation explanation. Extensive evaluations showed that our model outperforms the state-of-the-art models on the OpenDialKG dataset on multiple metrics.

Towards Classification of Legal Pharmaceutical Text using GAN-BERT
Tapan Auti | Rajdeep Sarkar | Bernardo Stearns | Atul Kr. Ojha | Arindam Paul | Michaela Comerford | Jay Megaro | John Mariano | Vall Herard | John P. McCrae
Proceedings of the First Computing Social Responsibility Workshop within the 13th Language Resources and Evaluation Conference

Pharmaceutical text classification is an important area of research for commercial and research institutions working in the pharmaceutical domain. Addressing this task is challenging due to the need of expert verified labelled data which can be expensive and time consuming to obtain. Towards this end, we leverage predictive coding methods for the task as they have been shown to generalise well for sentence classification. Specifically, we utilise GAN-BERT architecture to classify pharmaceutical texts. To capture the domain specificity, we propose to utilise the BioBERT model as our BERT model in the GAN-BERT framework. We conduct extensive evaluation to show the efficacy of our approach over baselines on multiple metrics.


Few-shot and Zero-shot Approaches to Legal Text Classification: A Case Study in the Financial Sector
Rajdeep Sarkar | Atul Kr. Ojha | Jay Megaro | John Mariano | Vall Herard | John P. McCrae
Proceedings of the Natural Legal Language Processing Workshop 2021

The application of predictive coding techniques to legal texts has the potential to greatly reduce the cost of legal review of documents, however, there is such a wide array of legal tasks and continuously evolving legislation that it is hard to construct sufficient training data to cover all cases. In this paper, we investigate few-shot and zero-shot approaches that require substantially less training data and introduce a triplet architecture, which for promissory statements produces performance close to that of a supervised system. This method allows predictive coding methods to be rapidly developed for new regulations and markets.


Unsupervised Deep Language and Dialect Identification for Short Texts
Koustava Goswami | Rajdeep Sarkar | Bharathi Raja Chakravarthi | Theodorus Fransen | John P. McCrae
Proceedings of the 28th International Conference on Computational Linguistics

Automatic Language Identification (LI) or Dialect Identification (DI) of short texts of closely related languages or dialects, is one of the primary steps in many natural language processing pipelines. Language identification is considered a solved task in many cases; however, in the case of very closely related languages, or in an unsupervised scenario (where the languages are not known in advance), performance is still poor. In this paper, we propose the Unsupervised Deep Language and Dialect Identification (UDLDI) method, which can simultaneously learn sentence embeddings and cluster assignments from short texts. The UDLDI model understands the sentence constructions of languages by applying attention to character relations which helps to optimize the clustering of languages. We have performed our experiments on three short-text datasets for different language families, each consisting of closely related languages or dialects, with very minimal training sets. Our experimental evaluations on these datasets have shown significant improvement over state-of-the-art unsupervised methods and our model has outperformed state-of-the-art LI and DI systems in supervised settings.

Suggest me a movie for tonight: Leveraging Knowledge Graphs for Conversational Recommendation
Rajdeep Sarkar | Koustava Goswami | Mihael Arcan | John P. McCrae
Proceedings of the 28th International Conference on Computational Linguistics

Conversational recommender systems focus on the task of suggesting products to users based on the conversation flow. Recently, the use of external knowledge in the form of knowledge graphs has shown to improve the performance in recommendation and dialogue systems. Information from knowledge graphs aids in enriching those systems by providing additional information such as closely related products and textual descriptions of the items. However, knowledge graphs are incomplete since they do not contain all factual information present on the web. Furthermore, when working on a specific domain, knowledge graphs in its entirety contribute towards extraneous information and noise. In this work, we study several subgraph construction methods and compare their performance across the recommendation task. We incorporate pre-trained embeddings from the subgraphs along with positional embeddings in our models. Extensive experiments show that our method has a relative improvement of at least 5.62% compared to the state-of-the-art on multiple metrics on the recommendation task.


A supervised approach to taxonomy extraction using word embeddings
Rajdeep Sarkar | John P. McCrae | Paul Buitelaar
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)