Natural Language Processing (NLP) is an established and dynamic field.Despite this, what constitutes NLP research remains debated.In this work, we address the question by quantitatively examining NLP research papers.We propose a taxonomy of research contributions and introduce NLPContributions, a dataset of nearly 2k NLP research paper abstracts, carefully annotated to identify scientific contributions and classify their types according to this taxonomy.We also introduce a novel task of automatically identifying contribution statements and classifying their types from research papers.We present experimental results for this task and apply our model to ∼29k NLP research papers to analyze their contributions, aiding in the understanding of the nature of NLP research.We show that NLP research has taken a winding path -with the focus on language and human-centric studies being prominent in the 1970s and 80s, tapering off in the 1990s and 2000s, and starting to rise again since the late 2010s.Alongside this revival, we observe a steady rise in dataset and methodological contributions since the 1990s, such that today, on average, individual NLP papers contribute in more ways than ever before.Our dataset and analyses offer a powerful lens for tracing research trends and offer potential for generating informed, datadriven literature surveys. 1

Understanding the fundamental concepts and trends in a scientific field is crucial for keeping abreast of its continuous advancement.In this study, we propose a systematic framework for analyzing the evolution of research topics in a scientific field using causal discovery and inference techniques.We define three variables to encompass diverse facets of the evolution of research topics within NLP and utilize a causal discovery algorithm to unveil the causal connections among these variables using observational data.Subsequently, we leverage this structure to measure the intensity of these relationships.By conducting extensive experiments on the ACL Anthology corpus, we demonstrate that our framework effectively uncovers evolutionary trends and the underlying causes for a wide range of NLP research topics.Specifically, we show that tasks and methods are primary drivers of research in NLP, with datasets following, while metrics have minimal impact. 1

Existing approaches for table annotation with entities and types either capture the structure of table using graphical models, or learn embeddings of table entries without accounting for the complete syntactic structure.We propose TabGCN, which uses Graph Convolutional Networks to capture the complete structure of tables, knowledge graph and the training annotations, and jointly learns embeddings for table elements as well as the entities and types.To account for knowledge incompleteness, TabGCN's embeddings can be used to discover new entities and types.Using experiments on 5 benchmark datasets, we show that TabGCN significantly outperforms multiple state-of-the-art baselines for table annotation, while showing promising performance on downstream table-related applications.

We study the problem of schema discovery for knowledge graphs.We propose a solution where an agent engages in multi-turn dialog with an expert for this purpose.Each minidialog focuses on a short natural language statement, and looks to elicit the expert's desired schema-based interpretation of that statement, taking into account possible augmentations to the schema.The overall schema evolves by performing dialog over a collection of such statements.We take into account the probability that the expert does not respond to a query, and model this probability as a function of the complexity of the query.For such mini-dialogs with response uncertainty, we propose a dialog strategy that looks to elicit the schema over as short a dialog as possible.By combining the notion of uncertainty sampling from active learning with generalized binary search, the strategy asks the query with the highest expected reduction of entropy.We show that this significantly reduces dialog complexity while engaging the expert in meaningful dialog.