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Found 44 papers, 20 papers with code
Effective Dimensionality Reduction for Word Embeddings
Pre-trained word embeddings are used in several downstream applications as well as for constructing representations for sentences, paragraphs and documents.
On Dimensional Linguistic Properties of the Word Embedding Space
Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties.
SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations
We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation.
P-SIF: Document Embeddings Using Partition Averaging
One of the key reasons is that a longer document is likely to contain words from many different topics; hence, creating a single vector while ignoring all the topical structure is unlikely to yield an effective document representation.
A Logic-Driven Framework for Consistency of Neural Models
While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples.
SumPubMed: Summarization Dataset of PubMed Scientific Articles
Most earlier work on text summarization is carried out on news article datasets.
Incorporating External Knowledge to Enhance Tabular Reasoning
Reasoning about tabular information presents unique challenges to modern NLP approaches which largely rely on pre-trained contextualized embeddings of text.
Two-Step Classification using Recasted Data for Low Resource Settings
We further improve the performance by using a joint-objective for classification and textual entailment.
Improving Document Classification with Multi-Sense Embeddings
Through extensive experiments on multiple real-world datasets, we show that SCDV-MS embeddings outperform previous state-of-the-art embeddings on multi-class and multi-label text categorization tasks.
Ranked #5 on
Document Classification
on Reuters-21578
(F1 metric)
IndicXNLI: Evaluating Multilingual Inference for Indian Languages
While Indic NLP has made rapid advances recently in terms of the availability of corpora and pre-trained models, benchmark datasets on standard NLU tasks are limited.
On Long-Tailed Phenomena in Neural Machine Translation
State-of-the-art Neural Machine Translation (NMT) models struggle with generating low-frequency tokens, tackling which remains a major challenge.
ChartCheck: Explainable Fact-Checking over Real-World Chart Images
Whilst fact verification has attracted substantial interest in the natural language processing community, verifying misinforming statements against data visualizations such as charts has so far been overlooked.
Text Summarization using Abstract Meaning Representation
With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding.
Unsupervised Semantic Abstractive Summarization
Automatic abstractive summary generation remains a significant open problem for natural language processing.
Unsupervised Contextualized Document Representation
In this paper, we address this issue by proposing SCDV+BERT(ctxd), a simple and effective unsupervised representation that combines contextualized BERT (Devlin et al., 2019) based word embedding for word sense disambiguation with SCDV soft clustering approach.
SLATE: A Sequence Labeling Approach for Task Extraction from Free-form Inked Content
We present SLATE, a sequence labeling approach for extracting tasks from free-form content such as digitally handwritten (or "inked") notes on a virtual whiteboard.
Unbiasing Review Ratings with Tendency Based Collaborative Filtering
User-generated contents{'} score-based prediction and item recommendation has become an inseparable part of the online recommendation systems.
TabPert: An Effective Platform for Tabular Perturbation
To truly grasp reasoning ability, a Natural Language Inference model should be evaluated on counterfactual data.
TabPert : An Effective Platform for Tabular Perturbation
To grasp the true reasoning ability, the Natural Language Inference model should be evaluated on counterfactual data.
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss
We extend the consistency of hierarchical classification algorithm over asymmetric tree distance loss.
Efficient Estimation of Generalization Error and Bias-Variance Components of Ensembles
We also demonstrate their usefulness in making design choices such as the number of classifiers in the ensemble and the size of a subset of data used for training that is needed to achieve a certain value of generalization error.
Leveraging Distributional Semantics for Multi-Label Learning
Our approach is novel in that it highlights interesting connections between label embedding methods used for multi-label learning and paragraph/document embedding methods commonly used for learning representations of text data.
User Bias Removal in Review Score Prediction
Review score prediction of text reviews has recently gained a lot of attention in recommendation systems.
Product Classification in E-Commerce using Distributional Semantics
Product classification is the task of automatically predicting a taxonomy path for a product in a predefined taxonomy hierarchy given a textual product description or title.
Equalizing Recourse across Groups
We measure recourse as the distance of an individual from the decision boundary of a classifier.
DeepSumm -- Deep Code Summaries using Neural Transformer Architecture
In this paper, we employ neural techniques to solve the task of source code summarizing and specifically compare NMT based techniques to more simplified and appealing Transformer architecture on a dataset of Java methods and comments.
INFOTABS: Inference on Tables as Semi-structured Data
In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them.
Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning
Our experiments demonstrate that a RoBERTa-based model, representative of the current state-of-the-art, fails at reasoning on the following counts: it (a) ignores relevant parts of the evidence, (b) is over-sensitive to annotation artifacts, and (c) relies on the knowledge encoded in the pre-trained language model rather than the evidence presented in its tabular inputs.
RETRONLU: Retrieval Augmented Task-Oriented Semantic Parsing
While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits from accuracy improvements to data efficiency for knowledge-focused tasks, such as question answering.
Unsupervised Document Representation using Partition Word-Vectors Averaging
One reason for this degradation is due to the fact that a longer document is likely to contain words from many different themes (or topics), and hence creating a single vector while ignoring all the thematic structure is unlikely to yield an effective representation of the document.
XInfoTabS: Evaluating Multilingual Tabular Natural Language Inference
In this paper, we use machine translation methods to construct a multilingual tabular NLI dataset, namely XINFOTABS, which expands the English tabular NLI dataset of INFOTABS to ten diverse languages.
Trans-KBLSTM: An External Knowledge Enhanced Transformer BiLSTM Model for Tabular Reasoning
Natural language inference on tabular data is a challenging task.
Right for the Right Reason: Evidence Extraction for Trustworthy Tabular Reasoning
On the downstream tabular inference task, using only the automatically extracted evidence as the premise, our approach outperforms prior benchmarks.
Bilingual Tabular Inference: A Case Study on Indic Languages
Existing research on Tabular Natural Language Inference (TNLI) exclusively examines the task in a monolingual setting where the tabular premise and hypothesis are in the same language.
Realistic Data Augmentation Framework for Enhancing Tabular Reasoning
We observed that our framework could generate human-like tabular inference examples, which could benefit training data augmentation, especially in the scenario with limited supervision.
Enhancing Tabular Reasoning with Pattern Exploiting Training
Recent methods based on pre-trained language models have exhibited superior performance over tabular tasks (e. g., tabular NLI), despite showing inherent problems such as not using the right evidence and inconsistent predictions across inputs while reasoning over the tabular data.
Leveraging Data Recasting to Enhance Tabular Reasoning
Creating challenging tabular inference data is essential for learning complex reasoning.
Evaluating Inter-Bilingual Semantic Parsing for Indian Languages
Despite significant progress in Natural Language Generation for Indian languages (IndicNLP), there is a lack of datasets around complex structured tasks such as semantic parsing.
MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments
Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens.
InfoSync: Information Synchronization across Multilingual Semi-structured Tables
The proposed method includes 1) Information Alignment to map rows and 2) Information Update for updating missing/outdated information for aligned tables across multilingual tables.
Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data
Thus, understanding and reasoning with numbers are essential skills for language models to solve different tasks.
TempTabQA: Temporal Question Answering for Semi-Structured Tables
Given these results, our dataset has the potential to serve as a challenging benchmark to improve the temporal reasoning capabilities of NLP models.
Multi-Set Inoculation: Assessing Model Robustness Across Multiple Challenge Sets
Language models, given their black-box nature, often exhibit sensitivity to input perturbations, leading to trust issues due to hallucinations.
Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering
Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with an amalgamation of structured tables and unstructured text is uncertain.
