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1. A Modular Vision Language Navigation and Manipulation Framework for Long Horizon Compositional Tasks in Indoor Environment

   https://doi.org/10.3389/frobt.2022.930486  

   Saha, Homagni; Fotouhi, Fateme; Liu, Qisai; Sarkar, Soumik (July 2022, Frontiers in Robotics and AI)

   In this paper we propose a new framework—MoViLan (Modular Vision and Language) for execution of visually grounded natural language instructions for day to day indoor household tasks. While several data-driven, end-to-end learning frameworks have been proposed for targeted navigation tasks based on the vision and language modalities, performance on recent benchmark data sets revealed the gap in developing comprehensive techniques for long horizon, compositional tasks (involving manipulation and navigation) with diverse object categories, realistic instructions and visual scenarios with non reversible state changes. We propose a modular approach to deal with the combined navigation and object interaction problem without the need for strictly aligned vision and language training data (e.g., in the form of expert demonstrated trajectories). Such an approach is a significant departure from the traditional end-to-end techniques in this space and allows for a more tractable training process with separate vision and language data sets. Specifically, we propose a novel geometry-aware mapping technique for cluttered indoor environments, and a language understanding model generalized for household instruction following. We demonstrate a significant increase in success rates for long horizon, compositional tasks over recent works on the recently released benchmark data set -ALFRED. 

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2. Toward Human Readable Prompt Tuning: Kubrick’s The Shining is a good movie, and a good prompt too?

   https://doi.org/10.18653/v1/2023.findings-emnlp.733  

   Shi, Weijia; Han, Xiaochuang; Gonen, Hila; Holtzman, Ari; Tsvetkov, Yulia; Zettlemoyer, Luke (January 2023, Association for Computational Linguistics)

   Large language models can perform downstream tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, but can also be learned with gradient-based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are in natural language. In this paper, we investigate common attributes shared by effective prompts in classification problems. We first propose a human readable prompt tuning method (FluentPrompt) based on Langevin dynamics that incorporates a fluency constraint to find a distribution of effective and fluent prompts. Our analysis reveals that effective prompts are topically related to the task domain and calibrate the prior probability of output labels. Based on these findings, we also propose a method for generating prompts using only unlabeled data, outperforming strong baselines by an average of 7.0{\%} accuracy across three tasks. 

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3. Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions

   Jin, Tian; Liu, Zhun; Yan, Shengjia; Eichenberger, Alexandre; Morency, Louis-Philippe (January 2020, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics)

   Transfer learning using ImageNet pre-trained models has been the de facto approach in a wide range of computer vision tasks. However, fine-tuning still requires task-specific training data. In this paper, we propose N3 (Neural Networks from Natural Language) - a new paradigm of synthesizing task-specific neural networks from language descriptions and a generic pre-trained model. N3 leverages language descriptions to generate parameter adaptations as well as a new task-specific classification layer for a pre-trained neural network, effectively “fine-tuning” the network for a new task using only language descriptions as input. To the best of our knowledge, N3 is the first method to synthesize entire neural networks from natural language. Experimental results show that N3 can out-perform previous natural-language based zero-shot learning methods across 4 different zero-shot image classification benchmarks. We also demonstrate a simple method to help identify keywords in language descriptions leveraged by N3 when synthesizing model parameters. 

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4. Parsing with Multilingual BERT, a Small Corpus, and a Small Treebank

   https://doi.org/10.18653/v1/2020.findings-emnlp.118  

   Chau, Ethan C.; Lin, Lucy H.; Smith, Noah A. (January 2020, : Findings of the Association for Computational Linguistics: EMNLP 2020)

   Pretrained multilingual contextual representations have shown great success, but due to the limits of their pretraining data, their benefits do not apply equally to all language varieties. This presents a challenge for language varieties unfamiliar to these models, whose labeled and unlabeled data is too limited to train a monolingual model effectively. We propose the use of additional language-specific pretraining and vocabulary augmentation to adapt multilingual models to low-resource settings. Using dependency parsing of four diverse low-resource language varieties as a case study, we show that these methods significantly improve performance over baselines, especially in the lowest-resource cases, and demonstrate the importance of the relationship between such models’ pretraining data and target language varieties. 

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5. Progressive Sentiment Analysis for Code-Switched Text Data

   Ranjan, Sudhanshu; Mekala, Dheeraj; Shang, Jingbo (January 2022, Findings of the Association for Computational Linguistics: EMNLP 2022)

   Multilingual transformer language models have recently attracted much attention from researchers and are used in cross-lingual transfer learning for many NLP tasks such as text classification and named entity recognition.However, similar methods for transfer learning from monolingual text to code-switched text have not been extensively explored mainly due to the following challenges:(1) Code-switched corpus, unlike monolingual corpus, consists of more than one language and existing methods can’t be applied efficiently,(2) Code-switched corpus is usually made of resource-rich and low-resource languages and upon using multilingual pre-trained language models, the final model might bias towards resource-rich language. In this paper, we focus on code-switched sentiment analysis where we have a labelled resource-rich language dataset and unlabelled code-switched data. We propose a framework that takes the distinction between resource-rich and low-resource language into account.Instead of training on the entire code-switched corpus at once, we create buckets based on the fraction of words in the resource-rich language and progressively train from resource-rich language dominated samples to low-resource language dominated samples. Extensive experiments across multiple language pairs demonstrate that progressive training helps low-resource language dominated samples. 

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