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PublicationUncovering Inconsistencies and Contradictions in Financial Reports using Large Language Models( 2023-12)
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PublicationContradiction Detection in Financial Reports( 2023-01-23)
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PublicationKPI-EDGAR: A Novel Dataset and Accompanying Metric for Relation Extraction from Financial Documents( 2022-12)
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PublicationTowards automating Numerical Consistency Checks in Financial Reports( 2022)
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PublicationAutomatic Indexing of Financial Documents via Information Extraction( 2021)The problem of extracting information from large volumes of unstructured documents is pervasive in the domain of financial business. Enterprises and investors need automatic methods that can extract information from these documents, particularly for indexing and efficiently retrieving information. To this end, we present a scalable end-to-end document processing system for indexing and information retrieval from large volumes of financial documents. While we show our system works for the use case of financial document processing, the entire system itself is agnostic of the document type and machine learning model type. Thus, it can be applied to any large-scale document processing task involving domain-specific extractors.
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PublicationTackling Contradiction Detection in German Using Machine Translation and End-to-End Recurrent Neural Networks( 2021)Natural Language Inference, and specifically Contradiction Detection, is still an unexplored topic with respect to German text. In this paper, we apply Recurrent Neural Network (RNN) methods to learn contradiction-specific sentence embeddings. Our data set for evaluation is a machine-translated version of the Stanford Natural Language Inference (SNLI) corpus. The results are compared to a baseline using unsupervised vectorization techniques, namely tf-idf and Flair, as well as state-of-the art transformer-based (MBERT) methods. We find that the end-to-end models outperform the models trained on unsupervised embeddings, which makes them the better choice in an empirical use case. The RNN methods also perform superior to MBERT on the translated data set.
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PublicationRatVec: A General Approach for Low-dimensional Distributed Vector Representations via Rational Kernels( 2019)We present a general framework, RatVec, for learning vector representations of non-numeric entities based on domain-specific similarity functions interpreted as rational kernels. We show competitive performance using k-nearest neighbors in the protein family classification task and in Dutch spelling correction. To promote re-usability and extensibility, we have made our code and pre-trained models available athttps://github.com/ratvec.
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PublicationTowards Contradiction Detection in German: A Translation-Driven Approach( 2019)With the recent advancements in Machine Learning based Natural Language Processing (NLP), language dependency has always been a limiting factor for a majority of NLP applications. Typically, models are trained for the English language due to the availability of very large labeled and unlabeled datasets, which also allow to fine tune models for that language. Contradiction Detection is one such problem that has found many practical applications in NLP and up to this point has only been studied in the context of English language. The scope of this paper is to examine a set of baseline methods for the Contradiction Detection task on German text. For this purpose, the well-known Stanford Natural Language Inference (SNLI) data set (110,000 sentence pairs) is machine-translated from English to German. We train and evaluate four classifiers on both the original and the translated data, using state-of-the-art textual data representations. Our main contribution is the first large-scale assessment for this problem in German, and a validation of machine translation as a data generation method. We also present a novel approach to learn sentence embeddings by exploiting the hidden states of an encoder-decoder Sequence-To-Sequence RNN trained for autoencoding or translation.
