Papers by Ruslan Salakhutdinov
Neural Models for Reasoning over Multiple Mentions Using Coreference (N18-2)
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| Challenge: | Existing Recurrent Neural Network (RNN) layers are biased towards short-term dependencies and hence not suited to such tasks. |
| Approach: | They propose a recurrent layer which is instead biased towards coreferent dependencies and uses coreference annotations extracted from an external system to connect entity mentions belonging to the same cluster. |
| Outcome: | The proposed layer improves performance on Wikihop, LAMBADA and the bAbi AI datasets with large gains when training data is scarce. |
Investigating the Working of Text Classifiers (C18-1)
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| Challenge: | Text classification is one of the most widely studied tasks in natural language processing. |
| Approach: | They propose to use large multilayer neural network models to compose meaning of sentences . they propose to disincentivize focusing on key lexicons to improve classification accuracy . |
| Outcome: | The proposed models learn to compose the meaning of the sentences or focus on key lexicons for classifying the document. |
Topological Sort for Sentence Ordering (2020.acl-main)
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| Challenge: | Recent work on sentence ordering task has framed it as a sequence prediction problem. |
| Approach: | They propose a new constraint solving problem and propose 'human evaluation' they propose to capture coherence in documents by arranging sentences in the correct order . |
| Outcome: | The proposed technique captures coherence in documents better than previous approaches. |
Towards Debiasing Sentence Representations (2020.acl-main)
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Paul Pu Liang, Irene Mengze Li, Emily Zheng, Yao Chong Lim, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Recent work has shown word-level embeddings reflect and propagate social biases present in training corpora. |
| Approach: | They propose a method to debias word embeddings to reduce biases at sentence level . they hope their work will inspire future research on characterizing and removing biase . |
| Outcome: | The proposed method reduces biases and preserves performance on downstream tasks such as sentiment analysis and natural language understanding. |
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization (P19-1)
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Paul Pu Liang, Zhun Liu, Yao-Hung Hubert Tsai, Qibin Zhao, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Existing methods to regularize multimodal data are imperfect due to imperfect modalities, missing entries or noise corruption. |
| Approach: | They propose a method to regularize multimodal data by tensor rank minimization . they use correlations between time and modalities to generate low-rank tenses . |
| Outcome: | The proposed model achieves good results across various levels of imperfection. |
Multimodal Transformer for Unaligned Multimodal Language Sequences (P19-1)
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Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, Ruslan Salakhutdinov
| Challenge: | Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors. |
| Approach: | They propose a multimodal model that extends the standard Transformer network to learn representations directly from unaligned multimodal streams. |
| Outcome: | The proposed model outperforms state-of-the-art methods on aligned and non-aligned data. |
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering (D18-1)
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Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning
| Challenge: | Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. |
| Approach: | They propose a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) the questions provide sentence-level supporting facts required for reasoning; and (4) a type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison. |
| Outcome: | The proposed dataset has 113k Wikipedia-based question-answer pairs and four key features that make it challenging for the latest QA systems. |
Don’t Copy the Teacher: Data and Model Challenges in Embodied Dialogue (2022.emnlp-main)
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| Challenge: | Embodied dialogue instruction following requires an agent to complete a complex sequence of tasks from a natural language exchange. |
| Approach: | They argue that imitation learning and low-level metrics are misleading . they compare existing models with IL and argue evaluation should focus on higher-level semantic goals . |
| Outcome: | The proposed model evaluations are based on three models and compare them with benchmarks . they show that existing models fail to ground query utterances, which are essential for task completion . |
Focused Attention Improves Document-Grounded Generation (2021.naacl-main)
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| Challenge: | Document grounded generation is the task of using the information provided in a document to improve text generation. |
| Approach: | They propose two new document grounded generation tasks that use information provided in a document to improve text generation. |
| Outcome: | The proposed models outperform existing methods on automated and human evaluation for closeness to reference and relevance to the document. |
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis (2022.findings-emnlp)
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Yuxin Xiao, Paul Pu Liang, Umang Bhatt, Willie Neiswanger, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Pre-trained language models (PLMs) have gained increasing popularity due to compelling prediction performance in diverse natural language processing tasks. |
| Approach: | They compare three popular options for encoding and Temp Scaling for PLMs . they recommend using Temp Loss as uncertainty quantifier and Focal Loss for fine-tuning . |
| Outcome: | Using pre-trained language models, we compare three options on NLP classification tasks and domain shift. |
Transformer Dissection: An Unified Understanding for Transformer’s Attention via the Lens of Kernel (D19-1)
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| Challenge: | Transformer is a powerful architecture that achieves superior performance on various sequence learning tasks, including neural machine translation, language understanding, and sequence prediction. |
| Approach: | They propose a new formulation of attention via the lens of the kernel which allows us to understand individual components of Transformer's attention. |
| Outcome: | The proposed model outperforms existing models on language understanding and sequence prediction tasks and is more efficient than existing models. |
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers (2022.acl-long)
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| Challenge: | Existing datasets for reading comprehension have deterministic answers, but questions in the real world do not always have definite answers. |
| Approach: | They propose a Question Answering (QA) dataset that contains complex questions with conditional answers. |
| Outcome: | The proposed dataset will motivate further research in answering complex questions over long documents. |
FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2022.acl-long)
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Yanan Zheng, Jing Zhou, Yujie Qian, Ming Ding, Chonghua Liao, Li Jian, Ruslan Salakhutdinov, Jie Tang, Sebastian Ruder, Zhilin Yang
| Challenge: | Existing evaluation protocols for few-shot natural language understanding (NLU) tasks are inconsistent and hinder fair comparison and measuring progress. |
| Approach: | They propose an evaluation framework that improves previous evaluation procedures in three key aspects, i.e., test performance, dev-test correlation, and stability. |
| Outcome: | The proposed framework improves evaluation procedures in three key aspects, i.e., performance, dev-test correlation, and stability. |
StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer (2021.naacl-main)
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Yiwei Lyu, Paul Pu Liang, Hai Pham, Eduard Hovy, Barnabás Póczos, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Existing methods for text style transfer focus on individual high-level semantic changes but do not offer fine-grained control of sentence structure, emphasis, and content. |
| Approach: | They propose a large-scale text style transfer benchmark with 21 fine-grained stylistic changes across atomic lexical, syntactic, semantic, and thematic transfers. |
| Outcome: | The proposed method allows modeling fine-grained changes as building blocks for more complex, high-level transfers. |
Style Transfer Through Back-Translation (P18-1)
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| Challenge: | a new method for automatic style transfer is proposed to preserve the meaning of the text while reducing stylistic properties. |
| Approach: | They propose a method for automatic style transfer that uses latent representations of the input sentence to preserve meaning while reducing stylistic properties. |
| Outcome: | The proposed method improves on sentiment, gender and political slant styles on three different styles. |
Exploring Controllable Text Generation Techniques (2020.coling-main)
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| Challenge: | Neural controllable text generation has a plethora of applications but there is no unifying theme. |
| Approach: | They propose a new schema for the control of attributes in the generation process by classifying it into five modules and providing an analysis on the advantages and disadvantages of these techniques. |
| Outcome: | The proposed frameworks can be used to control the attributes of natural sentences and to modulate the formality and politeness of emails. |
Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis (2020.emnlp-main)
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| Challenge: | Recent multimodal learning models with strong performances on human-centric tasks are often black-box with very limited interpretability. |
| Approach: | They propose a multimodal routing algorithm which dynamically adjusts weights between input and output modalities for each input sample. |
| Outcome: | The proposed model can interpret modality-prediction relationships globally and locally for each input sample while keeping competitive performance compared to state-of-the-art methods. |
Strong and Simple Baselines for Multimodal Utterance Embeddings (N19-1)
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| Challenge: | Human language is a rich multimodal signal consisting of spoken words, facial expressions, body gestures, and vocal intonations. |
| Approach: | They propose two simple but strong baselines to learn embeddings of multimodal utterances by factorizing the utterant into unimodal factors. |
| Outcome: | The proposed models show that they can be derived in closed form while maintaining simplicity and efficiency during learning and inference. |
Cross-modal Attention Congruence Regularization for Vision-Language Relation Alignment (2023.acl-long)
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| Challenge: | Despite recent progress towards scaling up multimodal vision-language models, these models struggle on compositional generalization benchmarks such as Winoground. |
| Approach: | They propose to use a cross-modal attention regularization loss to enforce relation alignment by capturing the semantic relation ‘in’ to match the visual attention from the mug to the grass. |
| Outcome: | The proposed approach improves Winoground Group score by 5.75 points . |
Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data (2021.acl-long)
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Paul Pu Liang, Terrance Liu, Anna Cai, Michal Muszynski, Ryo Ishii, Nick Allen, Randy Auerbach, David Brent, Ruslan Salakhutdinov, Louis-Philippe Morency
| Challenge: | Mental health conditions remain underdiagnosed in many countries despite access to advanced medical care . a new approach to learn mood markers from mobile data is needed to improve accuracy and improve learning from typed text. |
| Approach: | They propose to use mobile data to learn mood markers without identifying users through personal or protected attributes. |
| Outcome: | The proposed model obfuscates user identities while remaining predictive . future directions include better models and pre-learning from typed text . |
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context (P19-1)
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| Challenge: | Term memory networks (RNNs) are difficult to optimize due to gradient vanishing and explosion. |
| Approach: | They propose a neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. |
| Outcome: | The proposed method improves state-of-the-art performance on short and long sequences and generates coherent, novel text articles with thousands of tokens. |
Politeness Transfer: A Tag and Generate Approach (2020.acl-main)
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Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye
| Challenge: | Prior work on text style transfer has not focused on politeness as a style transfer task and we argue that defining it is cumbersome. |
| Approach: | They propose a task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. |
| Outcome: | The proposed model outperforms state-of-the-art methods on content preservation and style transfer accuracy. |
Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control (2023.findings-acl)
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| Challenge: | Existing methods for controlling the distribution of generated text only work with quantified distributions, which require pre-defined categories, proportions of the distribution, or an existing corpus following the desired distributions. |
| Approach: | They propose a few-shot human-in-the-loop training algorithm that allows distribution control for text generation via human feedback. |
| Outcome: | The proposed algorithm achieves state-of-the-art results on single topic/attribute and quantified distribution control compared to previous works. |
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text (D18-1)
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| Challenge: | Specialized neural models have been developed for extracting answers from text alone or Knowledge Bases (KBs) alone. |
| Approach: | They propose a novel model for extracting answers from a question-specific subgraph containing text and KB entities and relations. |
| Outcome: | The proposed model outperforms existing methods in a combination of a KB and entity-linked text in QA over a large text corpus. |
Case Study: Deontological Ethics in NLP (2021.naacl-main)
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| Challenge: | Recent work in natural language processing (NLP) has focused on ethical challenges . ethical foundations of NLP systems have not been explored . |
| Approach: | They propose to use deontological ethics to analyze ethical issues in natural language processing from the perspective of NLP. |
| Outcome: | The proposed ethical frameworks are based on the generalization principle and respect for autonomy through informed consent. |