The use of human speech to train LLMs poses privacy concerns due to these models' ability to generate samples that closely resemble artifacts in the training data. We propose a speaker privacy-preserving representation learning method through the Universal Speech Codec (USC), a computationally efficient codec that disentangles speech into: (i) privacy-preserving semantically rich representations, capturing content and speech paralinguistics, and (ii) residual acoustic and speaker representations that enable high-fidelity reconstruction. Evaluations show that USC's semantic representation preserves content, prosody, and sentiment, while removing identifiable traits. Additionally, we present an evaluation methodology for measuring privacy-preserving properties. We compare USC against other speech codecs and demonstrate its effectiveness on privacy-preserving representation learning, showcasing the trade-offs between speaker anonymization and paralinguistics retention.1

Identification and categorization of social media posts generated during disasters are crucial to reduce the suffering of the affected people. However, the lack of labeled data is a significant bottleneck in learning an effective categorization system for a disaster. This motivates us to study the problem as unsupervised domain adaptation (UDA) between a previous disaster with labeled data (source) and a current disaster (target). However, if the amount of labeled data available is limited, it restricts the learning capabilities of the model. To handle this challenge, we use limited labeled data along with abundantly available unlabeled data, generated during a source disaster to propose a novel two-part graph neural network (GNN). The first part extracts domain-agnostic global information by constructing a token-level graph across domains and the second part preserves local instance-level semantics. In our experiments, we show that the proposed method outperforms state-of-the-art techniques by 2.74% weighted F1 score on average on two standard public datasets in the area of disaster management. We also report experimental results for granular actionable multilabel classification datasets in disaster domain for the first time, on which we outperform BERT by 3.00% on average w.r.t. weighted F1. Additionally, we show that our approach can retain performance when minimal labeled data are available.

Systematically discovering semantic relationships in text is an important and extensively studied area in Natural Language Processing, with various tasks such as entailment, semantic similarity, etc. Decomposability of sentence-level scores via subsequence alignments has been proposed as a way to make models more interpretable. We study the problem of aligning components of sentences leading to an interpretable model for semantic textual similarity. In this paper, we introduce a novel pointer network based model with a sentinel gating function to align constituent chunks, which are represented using BERT. We improve this base model with a loss function to equally penalize misalignments in both sentences, ensuring the alignments are bidirectional. Finally, to guide the network with structured external knowledge, we introduce first-order logic constraints based on ConceptNet and syntactic knowledge. The model achieves an F1 score of 97.73 and 96.32 on the benchmark SemEval datasets for the chunk alignment task, showing large improvements over the existing solutions. Source code is available at https://github.com/manishb89/interpretable_sentence_similarity

Estimation of session duration for an e-commerce search engine is important for various downstream applications, including user satisfaction prediction, personalization, and diversification of search results. It has been shown in previous studies that search session length has a strong correlation with user's explore vs specific purchase intent. Based on previous work [14], we hypothesize that early prediction of session length distribution can be used to control the degree of explore vs exploit (loosely related to diversification v/s personalization) for Search Engine Result Pages (SERPs) in the user's session to follow. In this work, we try to early predict the user's session length, which will enable the control on explore v/s exploit of the search results. Towards this end, based on previous work and strong empirical evidence, we hypothesize session lengths are Weibull distributed and propose its parameters being modeled by a Recurrent Neural Network over actions in user's search sessions. Through experimentation, we demonstrate that our method performs better as compared to strong baselines for the same.

E-commerce customers express their purchase intents in several ways, some of which may use a different vocabulary than that of the product catalog. For example, the intent for "women maternity gown" is often expressed with the query, "ladies pregnancy dress". Search engines typically suffer from poor performance on such queries because of low overlap between query terms and specifications of the desired products. Past work has referred to these queries as vocabulary gap queries. In our experiments, we show that our technique significantly outperforms strong baselines and also show its real-world effectiveness with an online A/B experiment.