Search Results for author:
Found 34 papers, 28 papers with code
IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models
GPT4V, the best-performing VLM, achieves 62. 99% accuracy (4-shot) on the comprehension task and 49. 7% on the localization task (4-shot and Chain-of-Thought).
Ranked #1 on
Object Localization
on IllusionVQA
Repoformer: Selective Retrieval for Repository-Level Code Completion
Recent advances in retrieval-augmented generation (RAG) have initiated a new era in repository-level code completion.
On Leveraging Encoder-only Pre-trained Language Models for Effective Keyphrase Generation
This study addresses the application of encoder-only Pre-trained Language Models (PLMs) in keyphrase generation (KPG) amidst the broader availability of domain-tailored encoder-only models compared to encoder-decoder models.
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models
DeSel improves greedy search by an average of 4. 7% semantic F1 across five datasets.
CoCoMIC: Code Completion By Jointly Modeling In-file and Cross-file Context
While pre-trained language models (LM) for code have achieved great success in code completion, they generate code conditioned only on the contents within the file, i. e., in-file context, but ignore the rich semantics in other files within the same project, i. e., cross-file context, a critical source of information that is especially useful in modern modular software development.
PLUE: Language Understanding Evaluation Benchmark for Privacy Policies in English
Privacy policies provide individuals with information about their rights and how their personal information is handled.
Pre-trained Language Models for Keyphrase Generation: A Thorough Empirical Study
However, there lacks a systematic study of how the two types of approaches compare and how different design choices can affect the performance of PLM-based models.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
ContraCLM: Contrastive Learning For Causal Language Model
Specifically, we attain $44\%$ relative improvement on the Semantic Textual Similarity tasks and $34\%$ on Code-to-Code Search tasks.
FixEval: Execution-based Evaluation of Program Fixes for Programming Problems
To address this issue, we introduce FixEval, a benchmark comprising of buggy code submissions to competitive programming problems and their corresponding fixes.
Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages
In code generation, the model learns to do the opposite.
BanglaNLG and BanglaT5: Benchmarks and Resources for Evaluating Low-Resource Natural Language Generation in Bangla
This work presents BanglaNLG, a comprehensive benchmark for evaluating natural language generation (NLG) models in Bangla, a widely spoken yet low-resource language.
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies
Prior studies in privacy policies frame the question answering (QA) task as identifying the most relevant text segment or a list of sentences from a policy document given a user query.
Representation Learning for Resource-Constrained Keyphrase Generation
State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data.
CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs
LaSE is strongly correlated with ROUGE and, unlike ROUGE, can be reliably measured even in the absence of references in the target language.
Abstractive Text Summarization
Cross-Lingual Abstractive Summarization
+1
Retrieval Augmented Code Generation and Summarization
To mimic developers' code or summary generation behavior, we propose a retrieval augmented framework, REDCODER, that retrieves relevant code or summaries from a retrieval database and provides them as a supplement to code generation or summarization models.
Ranked #1 on
Code Generation
on CodeXGLUE - CodeSearchNet
(using extra training data)
AVATAR: A Parallel Corpus for Java-Python Program Translation
Automating program translation is of paramount importance in software migration, and recently researchers explored unsupervised approaches due to the unavailability of parallel corpora.
Syntax-augmented Multilingual BERT for Cross-lingual Transfer
In recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning.
CoDesc: A Large Code-Description Parallel Dataset
In this study, we present CoDesc -- a large parallel dataset composed of 4. 2 million Java methods and natural language descriptions.
Ranked #1 on
Code Search
on CoDesc
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training
Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer.
Text2App: A Framework for Creating Android Apps from Text Descriptions
We overcome this limitation by transforming natural language into an abstract intermediate formal language representing an application with a substantially smaller number of tokens.
Unified Pre-training for Program Understanding and Generation
Experiments on code summarization in the English language, code generation, and code translation in seven programming languages show that PLBART outperforms or rivals state-of-the-art models.
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla
In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature.
Intent Classification and Slot Filling for Privacy Policies
We refer to predicting the privacy practice explained in a sentence as intent classification and identifying the text spans sharing specific information as slot filling.
Simple or Complex? Learning to Predict Readability of Bengali Texts
Determining the readability of a text is the first step to its simplification.
PolicyQA: A Reading Comprehension Dataset for Privacy Policies
Prior studies in this domain frame the QA task as retrieving the most relevant text segment or a list of sentences from the policy document given a question.
GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction
Recent progress in cross-lingual relation and event extraction use graph convolutional networks (GCNs) with universal dependency parses to learn language-agnostic sentence representations such that models trained on one language can be applied to other languages.
Select, Extract and Generate: Neural Keyphrase Generation with Layer-wise Coverage Attention
Natural language processing techniques have demonstrated promising results in keyphrase generation.
A Transformer-based Approach for Source Code Summarization
Generating a readable summary that describes the functionality of a program is known as source code summarization.
Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages
We conduct experiments on cross-lingual dependency parsing where we train a dependency parser on a source language and transfer it to a wide range of target languages.
Context Attentive Document Ranking and Query Suggestion
We present a context-aware neural ranking model to exploit users' on-task search activities and enhance retrieval performance.
On Difficulties of Cross-Lingual Transfer with Order Differences: A Case Study on Dependency Parsing
Different languages might have different word orders.
Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks
Learning distributed sentence representations is one of the key challenges in natural language processing.
Multi-Task Learning for Document Ranking and Query Suggestion
We propose a multi-task learning framework to jointly learn document ranking and query suggestion for web search.
