Papers by Jianmo Ni
Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding (2020.emnlp-main)
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| Challenge: | Discourse analysis has been limited to small news corpora, but this study is expanding to tens of thousands of interviews. |
| Approach: | They propose a large-scale analysis of discourse in media dialog and its impact on dialog modeling with a focus on interrogative patterns and use of external knowledge. |
| Outcome: | The proposed model outperforms strong discourse-agnostic baselines for dialog modeling, generating more specific and topical responses in interview-style conversations. |
HYRR: Hybrid Infused Reranking for Passage Retrieval (2024.lrec-main)
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| Challenge: | Existing passage retrieval systems typically adopt a two-stage retrieve-then-rerank pipeline. |
| Approach: | They propose a framework for training robust reranking models using hybrid retrievers . they propose HYRR framework that allows users to select training data using hybrids . |
| Outcome: | The proposed framework is robust to different first-stage retrieval settings. |
Scalable and Accurate Dialogue State Tracking via Hierarchical Sequence Generation (D19-1)
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| Challenge: | Existing approaches to dialogue state tracking rely on pre-defined ontologies . however, these methods suffer from computational complexity that increases proportionally to the number of pre-determined slots. |
| Approach: | They propose a model that generates a sequence of belief states without the pre-defined ontology list. |
| Outcome: | The proposed model scales easily with the increasing number of pre-defined slots and domains and reaches the state-of-the-art performance on the multi-domain and single domain dialogue state tracking datasets. |
Personalized Review Generation By Expanding Phrases and Attending on Aspect-Aware Representations (P18-2)
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| Challenge: | Existing systems that use user and item identity as inputs for review generation are lacking in the field of natural language processing. |
| Approach: | They propose an encoder-decoder framework that generates personalized reviews by expanding short phrases provided as input to the system. |
| Outcome: | The proposed model learns representations capable of generating coherent and diverse reviews. |
Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models (2022.findings-acl)
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| Challenge: | Sentence embeddings are useful for language processing tasks, but it is unclear how to produce them from encoder-decoder models. |
| Approach: | They investigate the effects of scaling up sentence encoders to 11B parameters on sentence embeddings from text-to-text transformers (T5) . |
| Outcome: | The proposed models outperform the previous best models on both SentEval and SentGLUE transfer tasks. |
RISE: Leveraging Retrieval Techniques for Summarization Evaluation (2023.findings-acl)
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| Challenge: | Summarization evaluation approaches have relied on ROUGE for summarization, but they fall short of human evaluations. |
| Approach: | They propose a new approach to evaluate summaries by leveraging retrieval techniques . they use a dual-encoder retrieval setup to train a retrieval task . |
| Outcome: | The proposed method outperforms existing methods on two document summarization benchmarks and a long document summmarization test. |
SHARE: a System for Hierarchical Assistive Recipe Editing (2022.emnlp-main)
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| Challenge: | Existing recipe websites do not provide options for users with dietary restrictions . a growing population follows some form of dietary restriction, with many people following it for a variety of reasons . |
| Approach: | They propose a system for hierarchical assistive recipe editing that performs simultaneous ingredient substitution before generating natural-language steps using the edited ingredients. |
| Outcome: | The proposed system can adapt a recipe to satisfy a user-specified dietary constraint. |
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval (2024.naacl-long)
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| Challenge: | et al., 2020: performance of dense retrieval models in multilingual retrieval is limited due to uneven and scarce training data available across multiple languages. |
| Approach: | They propose a synthetic retrieval training dataset containing 33 languages for fine-tuning multilingual retrievers without human supervision. |
| Outcome: | The proposed model outperforms human-supervised retrieval models on three retrieval benchmarks. |
LongT5: Efficient Text-To-Text Transformer for Long Sequences (2022.findings-naacl)
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| Challenge: | Recent work has shown that increasing the input length or increasing model size can improve the performance of Transformer-based neural models. |
| Approach: | They propose a model that integrates attention ideas from long-input transformers and adopts pre-training strategies from summarization pre-train into the scalable T5 architecture. |
| Outcome: | The proposed model outperforms the original T5 models on several summarization and question answering tasks and achieves state-of-the-art results. |
Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)
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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang
| Challenge: | Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly. |
| Approach: | They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size . |
| Outcome: | The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly. |
A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding (2023.emnlp-main)
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Andrea Burns, Krishna Srinivasan, Joshua Ainslie, Geoff Brown, Bryan Plummer, Kate Saenko, Jianmo Ni, Mandy Guo
| Challenge: | Existing datasets for webpages contain only fragments of webpages . generative tasks like page description generation and section summarization are often left unstudied . |
| Approach: | They introduce a Wikipedia Webpage suite that contains 2M pages with all associated image, text, and structure data. |
| Outcome: | The proposed approach performs better than full attention with lower computational complexity. |
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering (N19-1)
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| Challenge: | Existing approaches to open-domain question answering struggle to retrieve indirectly related evidence when no direct evidence is provided. |
| Approach: | They propose a retriever-reader model that learns to attend on essential terms during the question answering process. |
| Outcome: | The proposed model achieves the state-of-the-art on multiple open-domain QA datasets and achieves a 'reader-reader' level. |
Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays (2020.findings-emnlp)
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| Challenge: | Existing work on report generation often trains encoder-decoder networks to generate complete reports, but such models are affected by data bias and face common issues inherent in text generation models. |
| Approach: | They propose a method to identify abnormal findings from radiology images and group them with unsupervised clustering and minimal rules. |
| Outcome: | The proposed method outperforms existing generation models on correctness and text generation metrics. |
Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)
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| Challenge: | Dual encoders have been used for question-answering and information retrieval tasks with good results. |
| Approach: | They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers . |
| Outcome: | The proposed architectures outperform SDE and ADE on QA retrieval tasks. |
Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (D19-1)
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| Challenge: | Existing approaches to generating reviews struggle to generate justifications that are relevant to users’ decision-making process. |
| Approach: | They propose an ‘extractive’ approach to identify review segments which justify users’ intentions and use it to distantly label massive review corpora and construct large-scale personalized recommendation justification datasets. |
| Outcome: | The proposed model can generate convincing and diverse justifications from massive review corpora and distantly label massive review data. |
Multi-stage Training with Improved Negative Contrast for Neural Passage Retrieval (2021.emnlp-main)
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| Challenge: | Existing neural firststage retrieval models overcome lexical gap issue by projecting query and document to a shared dense space. |
| Approach: | They propose a multi-stage framework for neural passage retrieval using synthetic data, negative sampling, and fusion techniques. |
| Outcome: | The proposed framework improves retrieval accuracy and enhances the negative contrast in both stages. |
Generating Personalized Recipes from Historical User Preferences (D19-1)
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| Challenge: | Existing methods to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes. |
| Approach: | They propose to expand a name and incomplete ingredient details into complete natural-text instructions aligned with the user’s historical preferences. |
| Outcome: | The proposed model generates plausible recipes from user-aware representations of recipes from 180K recipes and 700K interactions. |