Papers by Prakhar Gupta
Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation (2021.findings-acl)
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| Challenge: | Open-domain neural dialogue models have achieved high performance in response ranking and evaluation tasks. |
| Approach: | They propose methods for automatically creating adversarial negative training data . they use mask-and-fill and keyword-guided approaches to generate negative examples . |
| Outcome: | The proposed approaches outperform baseline models in providing informative negative examples for training dialogue systems. |
DialFact: A Benchmark for Fact-Checking in Dialogue (2022.acl-long)
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| Challenge: | Existing fact-checking models trained on non-dialogue data fail to perform well on this task. |
| Approach: | They propose a task of fact-checking in dialogue to improve fact- checking performance . they propose to use an annotated conversational claim and Wikipedia snippets as evidence . |
| Outcome: | The proposed task improves fact-checking performance in dialogue. |
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines (2023.findings-emnlp)
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Prakhar Gupta, Yang Liu, Di Jin, Behnam Hedayatnia, Spandana Gella, Sijia Liu, Patrick Lange, Julia Hirschberg, Dilek Hakkani-Tur
| Challenge: | Dialogue models are able to generate fluent and interesting responses, but they can be difficult to control and may produce non-engaging, unsafe results. |
| Approach: | They propose a framework for controlling dialogue model behavior using natural language rules, or guidelines, which provide information about the context they are applicable to and what should be included in the response. |
| Outcome: | The proposed framework is effective in three open-domain dialogue response generation tasks and is consistent with the developer's expectations and intent. |
Unsupervised Learning of Sentence Embeddings Using Compositional n-Gram Features (N18-1)
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| Challenge: | Currently, unsupervised word embeddings are routinely trained on large amounts of raw text data. |
| Approach: | They propose to use unsupervised word embeddings to train distributed representations of sentences. |
| Outcome: | The proposed method outperforms state-of-the-art models on most benchmark tasks and is robust to the produced general-purpose sentence embeddings. |
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation (2022.findings-naacl)
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| Challenge: | Existing methods for target-guided response generation are inconsistent with human judgement ratings. |
| Approach: | They propose a technique that finds a bridging path between the source and target and uses it to generate transition responses. |
| Outcome: | The proposed technique outperforms baselines on target-guided response generation task. |
Better Word Embeddings by Disentangling Contextual n-Gram Information (N19-1)
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| Challenge: | Pre-trained word vectors are ubiquitous in Natural Language Processing applications. |
| Approach: | They show that word embeddings with bigram and trigram embedds improve unigram embeds . they claim this removes contextual information from unigrammes, resulting in better unigraph embedders . |
| Outcome: | The proposed model outperforms competing models on a wide variety of tasks. |
Learning Word Vectors for 157 Languages (L18-1)
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| Challenge: | Distributed word representations, or word vectors, have been used in natural language processing for many tasks. |
| Approach: | They propose to use the encyclopedia Wikipedia and the common crawl corpus to train distributed word representations on large corpora and use them in downstream tasks. |
| Outcome: | The proposed model performs very well on 10 languages for which evaluation dataset exists. |
Controlling Dialogue Generation with Semantic Exemplars (2021.naacl-main)
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| Challenge: | Existing methods to control dialogue generation are manual labelling and manual editing of data. |
| Approach: | They propose a method to control dialogue generation using exemplar responses . they use semantic frames present in exemplars to guide response generation . |
| Outcome: | The proposed model improves coherence while preserving semantic meaning and conversation goals . exemplar responses are handwritten or strategically curated to promote highlevel goals without explicit labels . |
Leveraging Machine-Generated Rationales to Facilitate Social Meaning Detection in Conversations (2024.acl-long)
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| Challenge: | Existing models for language from a social perspective are gaining popularity . we present a generalizable classification approach that leverages Large Language Models . |
| Approach: | They propose a generalizable classification approach that leverages Large Language Models to detect social meaning in conversations. |
| Outcome: | The proposed approach improves on two social meaning detection tasks over 2,340 settings. |
Lightweight Cross-Lingual Sentence Representation Learning (2021.acl-long)
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| Challenge: | Existing models for learning fixed-dimensional cross-lingual sentence representations are impractical due to memory limitations. |
| Approach: | They propose a lightweight dual-transformer architecture with just 2 layers for generating memory-efficient cross-lingual sentence representations. |
| Outcome: | The proposed model improves performance on training tasks and improves memory efficiency. |
Using In-Context Learning to Improve Dialogue Safety (2023.findings-emnlp)
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Nicholas Meade, Spandana Gella, Devamanyu Hazarika, Prakhar Gupta, Di Jin, Siva Reddy, Yang Liu, Dilek Hakkani-Tur
| Challenge: | Recent work has highlighted safety issues with large neural-based conversational models. |
| Approach: | They propose a retrieval-based approach for reducing bias and toxicity in chatbot responses . they retrieve demonstrations of safe responses to similar dialogue contexts to generate a response . |
| Outcome: | The proposed method reduces bias and toxicity in three chatbot models . it can be used in compliment to existing dialogue safety approaches, such as RLHF. |
Revisiting In-Context Learning with Long Context Language Models (2025.findings-acl)
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| Challenge: | In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. |
| Approach: | They revisited previous studies using in-context learning techniques . they found that using a data augmentation approach, they significantly improved ICL performance . |
| Outcome: | The proposed approach significantly improves ICL performance on 18 datasets spanning 4 tasks . the proposed approach does not improve performance over a simple random sample selection method . |
Obtaining Better Static Word Embeddings Using Contextual Embedding Models (2021.acl-long)
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| Challenge: | Recent contextual word embeddings have prohibitively high computational cost in many use-cases and are hard to interpret. |
| Approach: | They propose a distillation method which is an extension of CBOW-based training and improves computational efficiency of NLP applications. |
| Outcome: | The proposed method outperforms existing models and existing models in terms of quality and performance. |
USB: A Unified Summarization Benchmark Across Tasks and Domains (2023.findings-emnlp)
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| Challenge: | Existing summarization benchmarks lack the rich annotations needed to address important problems related to control and reliability. |
| Approach: | They propose a Wikipedia-derived summarization benchmark with crowd-sourced annotations . they find that fine-tuned models outperform larger few-shot prompted language models . |
| Outcome: | The proposed model outperforms many-shot prompted language models on multiple tasks . the proposed model is based on Wikipedia annotations and can be used in other domains . |
InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuning (2022.emnlp-main)
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| Challenge: | Instruction tuning is emerging in NLP, but has not been explored for dialogue-related tasks. |
| Approach: | They propose an instruction tuning framework for dialogue that leverages natural language instructions with language models to induce zero-shot generalization on unseen tasks. |
| Outcome: | The proposed framework enables good zero-shot performance on unseen datasets and tasks such as dialogue evaluation and intent detection. |