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Found 68 papers, 39 papers with code
Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings
For this distinction, simple functions of vectors’ dimensions, called interactions, are used.
LLM Augmented LLMs: Expanding Capabilities through Composition
Foundational models with billions of parameters which have been trained on large corpora of data have demonstrated non-trivial skills in a variety of domains.
Self-Influence Guided Data Reweighting for Language Model Pre-training
Once the pre-training corpus has been assembled, all data samples in the corpus are treated with equal importance during LM pre-training.
Multimodal Modeling For Spoken Language Identification
Spoken language identification refers to the task of automatically predicting the spoken language in a given utterance.
Label Aware Speech Representation Learning For Language Identification
In this paper, we propose a novel framework of combining self-supervised representation learning with the language label information for the pre-training task.
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages
We evaluate commonly used models on the benchmark.
Salient Span Masking for Temporal Understanding
First, we find that SSM alone improves the downstream performance on three temporal tasks by an avg.
Cultural Re-contextualization of Fairness Research in Language Technologies in India
Recent research has revealed undesirable biases in NLP data and models.
UGIF: UI Grounded Instruction Following
We compare the performance of different LLMs including PaLM and GPT-3 and find that the end-to-end task completion rate is 48% for English UI but the performance drops to 32% for other languages.
Bootstrapping Multilingual Semantic Parsers using Large Language Models
Despite cross-lingual generalization demonstrated by pre-trained multilingual models, the translate-train paradigm of transferring English datasets across multiple languages remains to be a key mechanism for training task-specific multilingual models.
TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs
Recent years have witnessed much interest in temporal reasoning over knowledge graphs (KG) for complex question answering (QA), but there remains a substantial gap in human capabilities.
Re-contextualizing Fairness in NLP: The Case of India
In this paper, we focus on NLP fair-ness in the context of India.
Parameter-Efficient Finetuning for Robust Continual Multilingual Learning
We address this challenge by proposing LAFT-URIEL, a parameter-efficient finetuning strategy which aims to increase the number of languages on which the model improves after an update, while reducing the magnitude of loss in performance for the remaining languages.
Evaluating the Diversity, Equity and Inclusion of NLP Technology: A Case Study for Indian Languages
In order for NLP technology to be widely applicable, fair, and useful, it needs to serve a diverse set of speakers across the world's languages, be equitable, i. e., not unduly biased towards any particular language, and be inclusive of all users, particularly in low-resource settings where compute constraints are common.
Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages
This results in improved zero-shot transfer from related HRLs to LRLs without reducing HRL representation and accuracy.
When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer
While recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks, there is a lack of consensus in the community as to what shared properties between languages enable such transfer.
Multilingual Fact Linking
This makes it challenging to link KG facts to sentences in languages other than the limited set of languages.
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages Study
RelateLM uses transliteration to convert the unseen script of limited LRL text into the script of a Related Prominent Language (RPL) (Hindi in our case).
MergeDistill: Merging Pre-trained Language Models using Distillation
Pre-trained multilingual language models (LMs) have achieved state-of-the-art results in cross-lingual transfer, but they often lead to an inequitable representation of languages due to limited capacity, skewed pre-training data, and sub-optimal vocabularies.
Reordering Examples Helps during Priming-based Few-Shot Learning
Finally, we analyze the learned prompts to reveal novel insights, including the idea that two training examples in the right order alone can provide competitive performance for sentiment classification and natural language inference.
Question Answering Over Temporal Knowledge Graphs
Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (start and end times) on each edge in the KG.
Ranked #5 on
Question Answering
on CronQuestions
MuRIL: Multilingual Representations for Indian Languages
This can be explained by the fact that multilingual language models (LMs) are often trained on 100+ languages together, leading to a small representation of IN languages in their vocabulary and training data.
Spatial Reasoning from Natural Language Instructions for Robot Manipulation
Robots that can manipulate objects in unstructured environments and collaborate with humans can benefit immensely by understanding natural language.
Improving Multi-hop Question Answering over Knowledge Graphs using Knowledge Base Embeddings
In a separate line of research, KG embedding methods have been proposed to reduce KG sparsity by performing missing link prediction.
NILE : Natural Language Inference with Faithful Natural Language Explanations
In this work, we focus on the task of natural language inference (NLI) and address the following question: can we build NLI systems which produce labels with high accuracy, while also generating faithful explanations of its decisions?
Syntax-guided Controlled Generation of Paraphrases
In these methods, syntactic-guidance is sourced from a separate exemplar sentence.
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.
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
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)
A Re-evaluation of Knowledge Graph Completion Methods
Knowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs.
Ranked #25 on
Link Prediction
on FB15k-237
(MR metric)
Composition-based Multi-Relational Graph Convolutional Networks
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Ranked #22 on
Link Prediction
on FB15k-237
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE.
Ranked #11 on
Link Prediction
on YAGO3-10
CaRe: Open Knowledge Graph Embeddings
Organization of such triples in the form of a graph with noun phrases (NPs) as nodes and relation phrases (RPs) as edges results in the construction of Open Knowledge Graphs (OpenKGs).
Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs
In this paper, we explore GNNs for graph-based SSL of histograms.
Zero-shot Word Sense Disambiguation using Sense Definition Embeddings
To overcome this challenge, we propose Extended WSD Incorporating Sense Embeddings (EWISE), a supervised model to perform WSD by predicting over a continuous sense embedding space as opposed to a discrete label space.
Relating Simple Sentence Representations in Deep Neural Networks and the Brain
To the best of our knowledge, this is the first work showing that the MEG brain recording when reading a word in a sentence can be used to distinguish earlier words in the sentence.
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents.
Link Prediction in Hypergraphs using Graph Convolutional Networks
Additionally, there is need to represent the direction from reactants to products.
CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
Open Information Extraction (OpenIE) methods extract (noun phrase, relation phrase, noun phrase) triples from text, resulting in the construction of large Open Knowledge Bases (Open KBs).
Ranked #1 on
Noun Phrase Canonicalization
on Ambiguous Dataset
Dating Documents using Graph Convolution Networks
While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web.
Ranked #1 on
Document Dating
on APW
Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
Graph-based Semi-Supervised Learning (SSL) methods aim to address this problem by labeling a small subset of the nodes as seeds and then utilizing the graph structure to predict label scores for the rest of the nodes in the graph.
AD3: Attentive Deep Document Dater
Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc.
RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction.
Ranked #5 on
Relation Extraction
on NYT Corpus
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction
Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.
Ranked #4 on
Band Gap
on Materials Project
HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding
Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods.
Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks
Word embeddings have been widely adopted across several NLP applications.
HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
Towards Understanding the Geometry of Knowledge Graph Embeddings
These KG embedding methods represent KG entities and relations as vectors in a high-dimensional space.
ELDEN: Improved Entity Linking Using Densified Knowledge Graphs
Entity Linking (EL) systems aim to automatically map mentions of an entity in text to the corresponding entity in a Knowledge Graph (KG).
Ranked #3 on
Entity Linking
on CoNLL-Aida
Lovasz Convolutional Networks
We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.
Improving Distantly Supervised Relation Extraction using Word and Entity Based Attention
Relation extraction is the problem of classifying the relationship between two entities in a given sentence.
Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information
Besides the tensors, in many real world scenarios, side information is also available in the form of matrices which also grow in size with time.
CANDiS: Coupled & Attention-Driven Neural Distant Supervision
The unsupervised nature of this technique allows it to scale to web-scale relation extraction tasks, at the expense of noise in the training data.
KGEval: Accuracy Estimation of Automatically Constructed Knowledge Graphs
Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently.
Speeding up Reinforcement Learning-based Information Extraction Training using Asynchronous Methods
RLIE-DQN is a recently proposed Reinforcement Learning-based Information Extraction (IE) technique which is able to incorporate external evidence during the extraction process.
Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation
Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text.
KGEval: Estimating Accuracy of Automatically Constructed Knowledge Graphs
Automatic construction of large knowledge graphs (KG) by mining web-scale text datasets has received considerable attention recently.
Relation Schema Induction using Tensor Factorization with Side Information
To the best of our knowledge, this is the first application of tensor factorization for the RSI problem.
Want Answers? A Reddit Inspired Study on How to Pose Questions
We hypothesize that these factors contribute with varying degrees towards getting responses from others for a given question.
