Investigating Modality Bias in Audio Visual Video Parsing

We focus on the audio-visual video parsing (AVVP) problem that involves detecting audio and visual event labels with temporal boundaries. The task is especially challenging since it is weakly supervised with only event labels available as a bag of labels for each video. An existing state-of-the-art model for AVVP uses a hybrid attention network (HAN) to generate cross-modal features for both audio and visual modalities, and an attentive pooling module that aggregates predicted audio and visual segment-level event probabilities to yield video-level event probabilities. We provide a detailed analysis of modality bias in the existing HAN architecture, where a modality is completely ignored during prediction. We also propose a variant of feature aggregation in HAN that leads to an absolute gain in F-scores of about 2% and 1.6% for visual and audio-visual events at both segment-level and event-level, in comparison to the existing HAN model.

AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning

Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. In addition, finding the optimal hyper-parameter configuration, even with state-of-the-art (SOTA) hyper-parameter optimization (HPO) algorithms, can be time-consuming, requiring multiple training runs over the entire dataset for different possible sets of hyper-parameters. Our central insight is that using an informative subset of the dataset for model training runs involved in hyper-parameter optimization, allows us to find the optimal hyper-parameter configuration significantly faster. In this work, we propose AUTOMATA, a gradient-based subset selection framework for hyper-parameter tuning. We empirically evaluate the effectiveness of AUTOMATA in hyper-parameter tuning through several experiments on real-world datasets in the text, vision, and tabular domains. Our experiments show that using gradient-based data subsets for hyper-parameter tuning achieves significantly faster turnaround times and speedups of 3$\times$-30$\times$ while achieving comparable performance to the hyper-parameters found using the entire dataset.

BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets

Current semi-supervised learning (SSL) methods assume a balance between the number of data points available for each class in both the labeled and the unlabeled data sets. However, there naturally exists a class imbalance in most real-world datasets. It is known that training models on such imbalanced datasets leads to biased models, which in turn lead to biased predictions towards the more frequent classes. This issue is further pronounced in SSL methods, as they would use this biased model to obtain psuedo-labels (on the unlabeled data) during training. In this paper, we tackle this problem by attempting to select a balanced labeled dataset for SSL that would result in an unbiased model. Unfortunately, acquiring a balanced labeled dataset from a class imbalanced distribution in one shot is challenging. We propose BASIL (Balanced Active Semi-supervIsed Learning), a novel algorithm that optimizes the submodular mutual information (SMI) functions in a per-class fashion to gradually select a balanced dataset in an active learning loop. Importantly, our technique can be efficiently used to improve the performance of any SSL method. Our experiments on Path-MNIST and Organ-MNIST medical datasets for a wide array of SSL methods show the effectiveness of Basil. Furthermore, we observe that Basil outperforms the state-of-the-art diversity and uncertainty based active learning methods since the SMI functions select a more balanced dataset.

UDAAN - Machine Learning based Post-Editing tool for Document Translation

We introduce UDAAN, an open-source post-editing tool that can reduce manual editing efforts to quickly produce publishable-standard documents in different languages. UDAAN has an end-to-end Machine Translation (MT) plus post-editing pipeline wherein users can upload a document to obtain raw MT output. Further, users can edit the raw translations using our tool. UDAAN offers several advantages: a) Domain-aware, vocabulary-based lexical constrained MT. b) source-target and target-target lexicon suggestions for users. Replacements are based on the source and target texts lexicon alignment. c) Suggestions for translations are based on logs created during user interaction. d) Source-target sentence alignment visualisation that reduces the cognitive load of users during editing. e) Translated outputs from our tool are available in multiple formats: docs, latex, and PDF. Although we limit our experiments to English-to-Hindi translation for the current study, our tool is independent of the source and target languages. Experimental results based on the usage of the tools and users feedback show that our tool speeds up the translation time approximately by a factor of three compared to the baseline method of translating documents from scratch.

Submodlib: A Submodular Optimization Library

Submodular functions are a special class of set functions which naturally model the notion of representativeness, diversity, coverage etc. and have been shown to be computationally very efficient. A lot of past work has applied submodular optimization to find optimal subsets in various contexts. Some examples include data summarization for efficient human consumption, finding effective smaller subsets of training data to reduce the model development time (training, hyper parameter tuning), finding effective subsets of unlabeled data to reduce the labeling costs, etc. A recent work has also leveraged submodular functions to propose submodular information measures which have been found to be very useful in solving the problems of guided subset selection and guided summarization. In this work, we present Submodlib which is an open-source, easy-to-use, efficient and scalable Python library for submodular optimization with a C++ optimization engine. Submodlib finds its application in summarization, data subset selection, hyper parameter tuning, efficient training and more. Through a rich API, it offers a great deal of flexibility in the way it can be used. Source of Submodlib is available at https://github.com/decile-team/submodlib.

ALM-KD: Knowledge Distillation with noisy labels via adaptive loss mixing

Knowledge distillation is a technique where the outputs of a pretrained model, often known as the teacher model is used for training a student model in a supervised setting. The teacher model outputs being a richer distribution over labels should improve the student model's performance as opposed to training with the usual hard labels. However, the label distribution imposed by the logits of the teacher network may not be always informative and may lead to poor student performance. We tackle this problem via the use of an adaptive loss mixing scheme during KD. Specifically, our method learns an instance-specific convex combination of the teacher-matching and label supervision objectives, using meta learning on a validation metric signalling to the student `how much' of KD is to be used. Through a range of experiments on controlled synthetic data and real-world datasets, we demonstrate performance gains obtained using our approach in the standard KD setting as well as in multi-teacher and self-distillation settings.

Error Correction in ASR using Sequence-to-Sequence Models

Post-editing in Automatic Speech Recognition (ASR) entails automatically correcting common and systematic errors produced by the ASR system. The outputs of an ASR system are largely prone to phonetic and spelling errors. In this paper, we propose to use a powerful pre-trained sequence-to-sequence model, BART, further adaptively trained to serve as a denoising model, to correct errors of such types. The adaptive training is performed on an augmented dataset obtained by synthetically inducing errors as well as by incorporating actual errors from an existing ASR system. We also propose a simple approach to rescore the outputs using word level alignments. Experimental results on accented speech data demonstrate that our strategy effectively rectifies a significant number of ASR errors and produces improved WER results when compared against a competitive baseline.

Personalizing ASR with limited data using targeted subset selection

We study the task of personalizing ASR models to a target non-native speaker/accent while being constrained by a transcription budget on the duration of utterances selected from a large unlabelled corpus. We propose a subset selection approach using the recently proposed submodular mutual information functions, in which we identify a diverse set of utterances that match the target speaker/accent. This is specified through a few target utterances and achieved by modeling the relationship between the target subset and the selected subset using submodular mutual information functions. This method is applied at both the speaker and accent levels. We personalize the model by fine tuning it with utterances selected and transcribed from the unlabelled corpus. Our method is able to consistently identify utterances from the target speaker/accent using just speech features. We show that the targeted subset selection approach improves upon random sampling by as much as 2% to 5% (absolute) depending on the speaker and accent and is 2x to 4x more label-efficient compared to random sampling. We also compare with a skyline where we specifically pick from the target and our method generally outperforms the oracle in its selections.

Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming

A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time consuming to obtain. However, it has been shown that a small amount of labeled data, while insufficient to re-train a model, can be effectively used to generate human-interpretable labeling functions (LFs). These LFs, in turn, have been used to generate a large amount of additional noisy labeled data, in a paradigm that is now commonly referred to as data programming. However, previous approaches to automatically generate LFs make no attempt to further use the given labeled data for model training, thus giving up opportunities for improved performance. Moreover, since the LFs are generated from a relatively small labeled dataset, they are prone to being noisy, and naively aggregating these LFs can lead to very poor performance in practice. In this work, we propose an LF based reweighting framework \ouralgo{} to solve these two critical limitations. Our algorithm learns a joint model on the (same) labeled dataset used for LF induction along with any unlabeled data in a semi-supervised manner, and more critically, reweighs each LF according to its goodness, influencing its contribution to the semi-supervised loss using a robust bi-level optimization algorithm. We show that our algorithm significantly outperforms prior approaches on several text classification datasets.