Abstract

Causality detection and identification is centered on identifying semantic and cognitive connections in a sentence. In this paper, we describe the effort of team LTRC for Causal News Corpus - Event Causality Shared Task 2022 at the 5th Workshop on Challenges and Applications of Automated Extraction of Sociopolitical Events from Text (CASE 2022) (Tan et al., 2022a). The shared task consisted of two subtasks: 1) identifying if a sentence contains a causality relation, and 2) identifying spans of text that correspond to cause, effect and signals. We fine-tuned transformer-based models with adapters for both subtasks. Our best-performing models obtained a binary F1 score of 0.853 on held-out data for subtask 1 and a macro F1 score of 0.032 on held-out data for subtask 2. Our approach is ranked third in subtask 1 and fourth in subtask 2. The paper describes our experiments, solutions, and analysis in detail.

Abstract

Automatic extraction of “significant” components of a legal contract, has the potential to simplify the end user’s comprehension. In essence, “significant” pieces of information have 1) information pertaining to material/practical details about a specific contract and 2) information that is novel or comes as a “surprise” for a specific type of contract. It indicates that the significance of a component may be defined at an individual contract level and at a contract-type level. A component, sentence, or paragraph, may be considered significant at a contract level if it contains contract-specific information (CSI), like names, dates, or currency terms. At a contract-type level, components that deviate significantly from the norm for the type may be considered significant (type-specific information (TSI)). In this paper, we present approaches to extract “significant” components from a contract at both these levels. We attempt to do this by identifying patterns in a pool of documents of the same kind. Consequently, in our approach, the solution is formulated in two parts: identifying CSI using a BERT-based contract-specific information extractor and identifying TSI by scoring sentences in a contract for their likelihood. In this paper, we even describe the annotated corpus of contract documents that we created as a first step toward the development of such a language-processing system. We also release a dataset of contract samples containing sentences belonging to CSI and TSI.

Abstract

Komal Gandhar (The Gandhar Sublime/E-Flat, 1961) is the second of the Partition trilogy films made by Bengali filmmaker, Ritwik Ghatak (1925-1976). In this paper I argue that Ghatak uses music – centuries’ old traditional folk songs – to create a historical subjective consciousness in which the plot of the film can be played out. While plot progresses in time, an explicit durѐe is mapped on to the formal structuring of the film with the use of folk songs. The production of this duration, its specific grounding in a pre-modern Bengali aesthetic, and the centrality of loss, memory and performance in the plot, together posits a historical consciousness to stage the violence of a fractured relationship of citizen-subjects to their nation state. Komal Gandhar maps the possibility of a new living together in political engagement and theatre. Ghatak uses cinema’s ability to convey time as such to gesture towards a pre-modern historical consciousness. This historical consciousness can allow a new living together to materialize through political praxis. To live together again, class and community, history and myth, politics and art need to be reconstituted to allow for the recovery of past selves. Ghatak relocates the idea of citizen-subjecthood from urban nationalism to community formation. In this, he posits revolutionary politics and political theatre as the two cornerstones of an aesthetic-political praxis.

Abstract

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Abstract

In addition to the general neglect of women writers, Urdu literary historiography in both English and Urdu has historically privileged north Indian and Pakistani writers while overlooking the many Urdus south of Bombay and Bhopal. Next to no work exists in English on the Urdu writers of Hyderabad, and only a handful of texts have been translated into English —an astonishing neglect, considering their contribution to the study of gender, political cultures, and regional histories. Bibi’s Room studies the lives and work of three women writers from Hyderabad who wrote in Urdu: Zeenath Sajida, Najma Nikhat and Jeelani Bano. It addresses the absence of scholarship on Hyderabadi women writers in three ways: representative translations; short, nuanced biographies; and critical analyses of their oeuvres—all framed against twentieth-century Hyderabadi history, politics, culture, and society. The three writers showcased here offer rich portrayals of Hyderabadi urban culture as well as critiques of gender and patriarchy. Zeenath Sajida’s insights into Islam dramatically alter what we know of Muslim women’s engagements with fundamental theological questions. Sajida is also a skilled proponent of Urdu humour and satire, a genre notorious for its exclusion of women writers. Jeelani Bano’s oeuvre, spanning three schools of Urdu literature, makes vital contributions to our understanding of gender, class, communalism, and national identity. Najma Nikhat’s deodi stories powerfully narrate women’s lives across class in feudal aristocratic homes, and their participation in revolutionary struggles like the Telangana movement. The picture of Hyderabadi women’s lives that emerges generates new knowledge about the conditions in which women live, write, and resist, and expands our understanding of their public participation in South Asia. Bibi’s Room is also a welcome and valuable addition to studies of Urdu literature, South Asian feminism, translation, and the history and culture of Hyderabad.

Abstract

Transfer learning is a popular method for obtaining deep trained models for data-scarce face tasks such as head pose and emotion. However, current transfer learning methods are inefficient and time-consuming as they do not fully account for the relationships between related tasks. Moreover, the transferred model is large and computationally expensive. As an alternative, we propose ETL: a technique that efficiently transfers a pre-trained model to a new task by retaining only cross-task aware filters, resulting in a sparse transferred model. We demonstrate the effectiveness of ETL by transferring VGGFace, a popular face recognition model to four diverse face tasks. Our experiments show that we attain a size reduction up to 97% and an inference time reduction up to 94% while retaining 99.5% of the baseline transfer learning accuracy

Abstract

Conventional Reinforcement Learning (RL) algorithms assume the distribution of the data to be uniform or mostly uniform. However, this is not the case with most real-world applications like autonomous driving or in nature, where animals roam. Some objects are encountered frequently, and most of the remaining experiences occur rarely; the resulting distribution is called Zipfian. Taking inspiration from the theory of complementary learning systems, an architecture for learning from Zipfian distributions is proposed where long tail states are discovered in an unsupervised manner and states along with their recurrent activation are kept longer in episodic memory. The recurrent activations are then reinstated from episodic memory using a similarity search, giving weighted importance. The proposed architecture yields improved performance in a Zipfian task over conventional architectures. Our method outperforms IMPALA by a significant margin of 20.3% when maps/objects occur with a uniform distribution and by 50.2% on the rarest 20% of the distribution.

Abstract

Named Entity Recognition (NER) is a critical task in the field of Natural Language Processing (NLP) and is also a sub-task of Information Extraction. There has been a significant amount of work done in entity extraction and Named Entity Recognition for resource-rich languages. Entity extraction from code-mixed social media data like tweets from twitter complicates the problem due to its unstructured, informal, and incomplete information available in tweets. Here, we present work on NER in Kannada-English code-mixed social media corpus with corresponding named entity tags referring to Organisation (Org), Person (Pers), and Location (Loc). We experimented with machine learning classification models like Conditional Random Fields (CRF), Bi-LSTM, and Bi-LSTM-CRF models on our corpus.

Abstract

Often questions provided to open-domain question answering systems are ambiguous. Traditional QA systems that provide a single answer are incapable of answering ambiguous questions since the question may be interpreted in several ways and may have multiple distinct answers. In this paper, we address multi-answer retrieval which entails retrieving passages that can capture majority of the diverse answers to the question. We propose a re-ranking based approach using Determinantal point processes utilizing BERT as kernels. Our method jointly considers query-passage relevance and passage-passage correlation to retrieve passages that are both query-relevant and diverse. Results demonstrate that our re-ranking technique outperforms state-of-the-art method on the AmbigQA dataset.

Abstract

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model perfor- mances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. How- ever, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reser- voir, Bhadra reservoir Tunga-Bhadra River, located in India.