Search Results for author:
Found 31 papers, 15 papers with code
AliEdalat at SemEval-2022 Task 4: Patronizing and Condescending Language Detection using Fine-tuned Language Models, BERT+BiGRU, and Ensemble Models
In PCL category detection, our model outperforms the baseline model and achieves an F1-score of 0. 2531.
Ranked #4 on
Binary Condescension Detection
on DPM
Binary Condescension Detection
Multi-label Condescension Detection
uTeBC-NLP at SemEval-2024 Task 9: Can LLMs be Lateral Thinkers?
Inspired by human cognition, Jiang et al.(2023c) create a benchmark for assessing LLMs' lateral thinking-thinking outside the box.
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT
This paper explores the efficacy of large language models (LLMs) for Persian.
Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers
Neural networks have revolutionized language modeling and excelled in various downstream tasks.
DecompX: Explaining Transformers Decisions by Propagating Token Decomposition
An emerging solution for explaining Transformer-based models is to use vector-based analysis on how the representations are formed.
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
This paper proposes LM-CPPF, Contrastive Paraphrasing-guided Prompt-based Fine-tuning of Language Models, which leverages prompt-based few-shot paraphrasing using generative language models, especially large language models such as GPT-3 and OPT-175B, for data augmentation.
Ranked #2 on
Sentiment Analysis
on CR
PEACH: Pre-Training Sequence-to-Sequence Multilingual Models for Translation with Semi-Supervised Pseudo-Parallel Document Generation
Multilingual pre-training on monolingual data ignores the availability of parallel data in many language pairs.
BERT on a Data Diet: Finding Important Examples by Gradient-Based Pruning
In this work, we employ these two metrics for the first time in NLP.
Looking at the Overlooked: An Analysis on the Word-Overlap Bias in Natural Language Inference
It has been shown that NLI models are usually biased with respect to the word-overlap between premise and hypothesis; they take this feature as a primary cue for predicting the entailment label.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
GlobEnc: Quantifying Global Token Attribution by Incorporating the Whole Encoder Layer in Transformers
There has been a growing interest in interpreting the underlying dynamics of Transformers.
Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages
Large pre-trained language models (PLMs) are therefore assumed to encode metaphorical knowledge useful for NLP systems.
PerCQA: Persian Community Question Answering Dataset
In this paper, we present PerCQA, the first Persian dataset for CQA.
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
Cross-Domain Generalization Through Memorization: A Study of Nearest Neighbors in Neural Duplicate Question Detection
Duplicate question detection (DQD) is important to increase efficiency of community and automatic question answering systems.
Quantifying the Contextualization of Word Representations with Semantic Class Probing
Pretrained language models have achieved a new state of the art on many NLP tasks, but there are still many open questions about how and why they work so well.
Increasing Robustness to Spurious Correlations using Forgettable Examples
Neural NLP models tend to rely on spurious correlations between labels and input features to perform their tasks.
Natural Language Inference
Natural Language Understanding
+2
Unsupervised Domain Adaptation of Contextual Embeddings for Low-Resource Duplicate Question Detection
In this paper, we apply BERT to DQD and advance it by unsupervised adaptation to StackExchange domains using self-supervised learning.
Toward Understanding The Effect of Loss Function on The Performance of Knowledge Graph Embedding
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
Toward Understanding The Effect Of Loss function On Then Performance Of Knowledge Graph Embedding
We show that by a proper selection of the loss function for training the TransE model, the main limitations of the model are mitigated.
Probing for Semantic Classes: Diagnosing the Meaning Content of Word Embeddings
Word embeddings typically represent different meanings of a word in a single conflated vector.
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity Typing
For representation, we consider representations based on the context distribution of the entity (i. e., on its embedding), on the entity's name (i. e., on its surface form) and on its description in Wikipedia.
Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing
We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding.
Recurrent One-Hop Predictions for Reasoning over Knowledge Graphs
Large scale knowledge graphs (KGs) such as Freebase are generally incomplete.
Corpus-level Fine-grained Entity Typing
This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist".
Multi-level Representations for Fine-Grained Typing of Knowledge Base Entities
Entities are essential elements of natural language.
Noise Mitigation for Neural Entity Typing and Relation Extraction
For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction.
Corpus-level Fine-grained Entity Typing Using Contextual Information
This paper addresses the problem of corpus-level entity typing, i. e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist".
Intrinsic Subspace Evaluation of Word Embedding Representations
We introduce a new methodology for intrinsic evaluation of word representations.
