Papers by Ramesh Nallapati
ReCode: Robustness Evaluation of Code Generation Models (2023.acl-long)
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Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, Bing Xiang
| Challenge: | Existing work on robustness in text or code tasks has focused on classification, while robustness for code generation tasks is an uncharted area. |
| Approach: | They propose a robustness evaluation benchmark for code generation models that customizes over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. |
| Outcome: | The proposed model performs better on human annotators and on SOTA models with human annnotators. |
End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems (2020.emnlp-main)
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Siamak Shakeri, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Feng Nan, Zhiguo Wang, Ramesh Nallapati, Bing Xiang
| Challenge: | Existing approaches for synthetic QA data generation have limited or no success in improving the downstream Reading Comprehension task. |
| Approach: | They propose an end-to-end approach for synthetic QA data generation using a transformer-based encoder-decoder network that is trained end- to-end to generate both answers and questions. |
| Outcome: | The proposed model outperforms current state-of-the-art methods in the domain adaptation of QA models. |
Topic Modeling with Wasserstein Autoencoders (P19-1)
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| Challenge: | Existing probabilistic topic models are based on latent Dirichlet allocations and collapsed Gibbs sampling. |
| Approach: | They propose a novel topic model that enforces Dirichlet prior on latent document-topic vectors and a kernel kernel to minimize the Maximum Mean Discrepancy (MMD) They propose to measure the diversity of the produced topics and to use the widely used coherence measure NPMI to evaluate topic quality. |
| Outcome: | The proposed model performs better than existing topic models on real datasets. |
Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction (2021.acl-long)
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Yifan Gao, Henghui Zhu, Patrick Ng, Cicero Nogueira dos Santos, Zhiguo Wang, Feng Nan, Dejiao Zhang, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang
| Challenge: | Open-domain question answering is a task to answer questions using passages with diverse topics. |
| Approach: | They propose a model that aggregates evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions. |
| Outcome: | The proposed model achieves state-of-the-art performance on AmbigQA dataset and shows competitive performance on NQ-Open and TriviaQA. |
Retrieval, Re-ranking and Multi-task Learning for Knowledge-Base Question Answering (2021.eacl-main)
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| Challenge: | Existing work on question answering over knowledge bases limited the search space to a subset of KBs . a retrieval-and-rerank framework is used to access KB and rerank retrieved candidates with more powerful neural networks. |
| Approach: | They propose to share a BERT encoder across all three sub-tasks and define task-specific layers on top of the shared layer. |
| Outcome: | The proposed method improves accuracy and accuracy on the SimpleQuestions dataset and the FreebaseQA dataset. |
BASS: Batched Attention-optimized Speculative Sampling (2024.findings-acl)
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Haifeng Qian, Sujan Kumar Gonugondla, Sungsoo Ha, Mingyue Shang, Sanjay Krishna Gouda, Ramesh Nallapati, Sudipta Sengupta, Xiaofei Ma, Anoop Deoras
| Challenge: | Speculative decoding has emerged as a powerful method to improve latency and throughput in hosting large language models. |
| Approach: | They propose a batched speculative decoding system that generates sequences at an average speed of 5.8ms per token and a batch size of 8 at a 2.15 speed-up over optimized regular decoding. |
| Outcome: | The proposed system achieves state-of-the-art latency and speed-up over optimized regular decoding. |
Entity-level Factual Consistency of Abstractive Text Summarization (2021.eacl-main)
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Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang
| Challenge: | Existing models exhibit entity hallucination, generating names of entities that are not present in the source document. |
| Approach: | They propose to use entity-level factual consistency to improve model quality . they propose to filter the training data to reduce entity hallucination problem . |
| Outcome: | The proposed model can reduce the entity hallucination problem by filtering the training data. |
Token Alignment via Character Matching for Subword Completion (2024.findings-acl)
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Ben Athiwaratkun, Shiqi Wang, Mingyue Shang, Yuchen Tian, Zijian Wang, Sujan Kumar Gonugondla, Sanjay Krishna Gouda, Robert Kwiatkowski, Ramesh Nallapati, Parminder Bhatia, Bing Xiang
| Challenge: | Generative models struggle with prompts corresponding to partial tokens due to tokenization, where partial token is out-of-distribution during inference. |
| Approach: | They propose a method to alleviate tokenization artifact on text completion by backtracking to the last complete tokens and aligning subsequent generations to match with the prompt. |
| Outcome: | The proposed method shows that it improves on partial token scenarios with only a minor time increase. |
Margin-aware Unsupervised Domain Adaptation for Cross-lingual Text Labeling (2020.findings-emnlp)
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Dejiao Zhang, Ramesh Nallapati, Henghui Zhu, Feng Nan, Cicero Nogueira dos Santos, Kathleen McKeown, Bing Xiang
| Challenge: | Existing approaches to learn a model from labeled data are expensive or prohibitive. |
| Approach: | They propose an unsupervised domain adaptation algorithm that leverages labeled data in a source domain to learn a well-performing model in . they use the Margin Disparity Discrepancy algorithm to optimize the margin loss on the source domain. |
| Outcome: | The proposed approach improves on a recent theoretical work on cross-lingual document classification and NER by a large margin. |
Template-Based Question Generation from Retrieved Sentences for Improved Unsupervised Question Answering (2020.acl-main)
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| Challenge: | Question Answering (QA) is a field of increasing demand due to the availability of information online. |
| Approach: | They propose an unsupervised approach to training QA models with generated pseudo-training data by applying a simple template on a related sentence rather than the original context sentence. |
| Outcome: | The proposed approach improves the performance of a QA model on generated pseudo-training data. |
Exploring Continual Learning for Code Generation Models (2023.acl-short)
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Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Parminder Bhatia, Xiaofei Ma, Ramesh Nallapati, Murali Krishna Ramanathan, Mohit Bansal, Bing Xiang
| Challenge: | Large-scale code generation models such as Copilot and CodeT5 are expensive to train and re-train. |
| Approach: | They propose a benchmark for Continual Learning (CL) that covers a wide range of tasks with different input and output programming languages. |
| Outcome: | The proposed method improves on Prompt Pooling with Teacher Forcing, which suffers catastrophic forgetting due to stark distribution shifts in coding tasks. |
Multitask Pretraining with Structured Knowledge for Text-to-SQL Generation (2023.acl-long)
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Robert Giaquinto, Dejiao Zhang, Benjamin Kleiner, Yang Li, Ming Tan, Parminder Bhatia, Ramesh Nallapati, Xiaofei Ma
| Challenge: | Existing methods for learning representations of structured knowledge are limited to the minority of people with technical skills. |
| Approach: | They propose a large pretraining dataset and strategy for learning representations of text, tables, and SQL code that leverages the entire context of the problem. |
| Outcome: | The proposed model improves on two SQL tasks and shows a 1.7 and 2.2 percentage point improvement over existing methods. |
Coherence-Aware Neural Topic Modeling (D18-1)
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| Challenge: | Topic models are evaluated for their ability to describe documents well (i.e. low perplexity) topic coherence is not optimized for and is only evaluated after training. |
| Approach: | They propose to incorporate a topic coherence objective into the training process by incorporating a coherency objective into a model. |
| Outcome: | The proposed model exhibits similar level of perplexity as baseline models but significantly higher topic coherence. |
Supporting Clustering with Contrastive Learning (2021.naacl-main)
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Dejiao Zhang, Feng Nan, Xiaokai Wei, Shang-Wen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew O. Arnold, Bing Xiang
| Challenge: | Unsupervised clustering aims at discovering the semantic categories of data according to some distance measured in the representation space, but different categories overlap with each other at the beginning of the learning process. |
| Approach: | They propose a framework to leverage contrastive learning to promote better separation between different categories by optimizing a clustering objective defined in the representation space. |
| Outcome: | The proposed framework improves state-of-the-art accuracy and normalized mutual information on short text clustering and combines top-down and bottom-up instance discrimination to achieve better distances. |
Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding (2022.emnlp-industry)
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Jun Wang, Patrick Ng, Alexander Hanbo Li, Jiarong Jiang, Zhiguo Wang, Bing Xiang, Ramesh Nallapati, Sudipta Sengupta
| Challenge: | Recent research on Text-to-SQL semantic parsing relies on parser or heuristic based approach to understand natural language query. |
| Approach: | They propose a general-purpose, modular neural semantic parsing framework that is based on token-level fine-grained query understanding. |
| Outcome: | The proposed framework outperforms the state-of-the-art model by 2.7% on a WikiTableQuestions test set. |
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization (2022.acl-short)
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Zheng Li, Zijian Wang, Ming Tan, Ramesh Nallapati, Parminder Bhatia, Andrew Arnold, Bing Xiang, Dan Roth
| Challenge: | Empirical analyses show that pre-trained sequence-to-sequence models can achieve a 16.5x model footprint compression ratio with little performance drop relative to full-precision counterparts. |
| Approach: | They propose to distill and quantize pre-trained sequence-to-sequence models to reduce memory and latency requirements. |
| Outcome: | Empirical results show that the proposed model achieves 16.5x model footprint compression ratio with little performance drop relative to full-precision counterparts on multiple summarization and QA datasets. |
Improving Factual Consistency of Abstractive Summarization via Question Answering (2021.acl-long)
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Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, Bing Xiang
| Challenge: | Recent studies show that about 30% of summaries generated by neural text summarization suffer from fact fabrication. |
| Approach: | They propose an automatic evaluation metric to measure factual consistency and a learning algorithm that maximizes the metric during model training. |
| Outcome: | The proposed method improves factual consistency and overall quality of summarization models. |
Beyond [CLS] through Ranking by Generation (2020.emnlp-main)
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| Challenge: | Recent work on generative ranking models for Information Retrieval has focused on discriminative methods that learn a similarity function to compare questions and candidates answers. |
| Approach: | They propose to use a language model to train a ranking function that model the semantic similarity of documents and queries instead of discriminative ranking functions. |
| Outcome: | The proposed approaches are as effective as state-of-the-art discriminative models for the answer selection task and show unlikelihood losses are reduced for IR. |
ContraCLM: Contrastive Learning For Causal Language Model (2023.acl-long)
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Nihal Jain, Dejiao Zhang, Wasi Uddin Ahmad, Zijian Wang, Feng Nan, Xiaopeng Li, Ming Tan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Xiaofei Ma, Bing Xiang
| Challenge: | Existing studies show that causal language models lack expressiveness due to poor discrimination ability. |
| Approach: | They propose a contrastive learning framework that enhances discrimination of representations and bridges the gap with encoder-only models. |
| Outcome: | The proposed framework improves discrimination and source code generation capabilities on a variety of downstream tasks. |
Domain Adaptation with BERT-based Domain Classification and Data Selection (D19-61)
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| Challenge: | Modern deep neural models with millions of parameters can easily adapt to a new learning task and dataset when enough supervision is given. |
| Approach: | They propose a domain adaptation framework based on curriculum learning and domain-discriminative data selection. |
| Outcome: | The proposed framework outperforms discrepancy-based methods on transfer tasks while consuming only fraction of training budget. |
CoCoMIC: Code Completion by Jointly Modeling In-file and Cross-file Context (2024.lrec-main)
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Yangruibo Ding, Zijian Wang, Wasi U. Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, Bing Xiang
| Challenge: | Pre-trained language models (LMs) for code have shown promising performance in code completion tasks but ignore the rich semantics in other files within the same project. |
| Approach: | They propose a framework that jointly learns the in-file and cross-file context on top of code LMs and a static-analysis-based tool that locates and retrieves the most relevant project-level cross- file context for code completion. |
| Outcome: | The proposed framework improves existing code LMs with a 33.94% relative increase in exact match and 28.69% in identifier matching when the cross-file context is provided. |
Pairwise Supervised Contrastive Learning of Sentence Representations (2021.emnlp-main)
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| Challenge: | Recent efforts to improve sentence representation learning have a common weakness . siamese or triplet loss only learns from individual sentence pairs or tripletes . |
| Approach: | They propose a discrimination-based approach to bridge entailment and contradiction understanding with categorical concept encoding. |
| Outcome: | The proposed method outperforms the state-of-the-art method on downstream tasks . it improves 10%–13% on clustering tasks and 5%–6% on STS tasks compared with the previous method . |
Efficient Few-Shot Fine-Tuning for Opinion Summarization (2022.findings-naacl)
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| Challenge: | Abstractive summarization models are typically pre-trained on large amounts of generic texts . large annotated datasets of reviews paired with reference summaries are not available . |
| Approach: | They propose a few-shot method which uses adapters to store in-domain knowledge . they pre-train adapters on unannotated customer reviews and fine-tune them on annotated datasets . |
| Outcome: | The proposed method can store in-domain knowledge and improves on large annotated reviews . it improves coherence and redundancies on the Amazon and Yelp datasets . |
Multi-passage BERT: A Globally Normalized BERT Model for Open-domain Question Answering (D19-1)
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| Challenge: | Existing studies have shown that BERT models can find answers from multiple passages . however, the results of these studies are still unaddressed. |
| Approach: | They propose a multi-passage BERT model to globally normalize answer scores across all passages of the same question. |
| Outcome: | The proposed model outperforms state-of-the-art models on four benchmarks. |