Multilingual ASR offers training, deployment and overall performance benefits, but models trained via simple data pooling are known to suffer from cross-lingual interference. Oracle language information (exact-prior) and language-specific parameters are usually leveraged to overcome this, but such approaches cannot enable seamless, truly multilingual experiences. Existing methods try to overcome this limitation by relying on inferred language information or language agnostic mixture-of-experts, but they incur additional runtime complexity and/or training cost in addition to being less effective in streaming scenarios. Building on previous studies where models were trained to handle mixed-prior (knowledge that the underlying language belongs to a known group), we propose Attentive Multilingual Speech Encoding (AMuSE), a training framework designed to match exact-prior performance even in the absence of underlying language information at runtime (zero-prior), thereby making the model prior-agnostic. Leveraging AMuSE, we build a zero-prior enabled LLM-based ASR system that outperforms several exact-prior driven state-of-the-art benchmarks.

Comprehending the overall intent of an utterance helps a listener recognize the individual words spoken. Inspired by this fact, we perform a novel study of the impact of explicitly incorporating intent representations as additional information to improve a recurrent neural network-transducer (RNN-T) based automatic speech recognition (ASR) system. An audio-to-intent (A2I) model encodes the intent of the utterance in the form of embeddings or posteriors, and these are used as auxiliary inputs for RNN-T training and inference. Experimenting with a 50k-hour far-field English speech corpus, this study shows that when running the system in non-streaming mode, where intent representation is extracted from the entire utterance and then used to bias streaming RNN-T search from the start, it provides a 5.56% relative word error rate reduction (WERR). On the other hand, a streaming system using per-frame intent posteriors as extra inputs for the RNN-T ASR system yields a 3.33% relative WERR. A further detailed analysis of the streaming system indicates that our proposed method brings especially good gain on media-playing related intents (e.g. 9.12% relative WERR on PlayMusicIntent).

Document date is essential for many important tasks, such as document retrieval, summarization, event detection, etc.While existing approaches for these tasks assume accurate knowledge of the document date, this is not always available, especially for arbitrary documents from the Web.Document Dating is a challenging problem which requires inference over the temporal structure of the document.Prior document dating systems have largely relied on handcrafted features while ignoring such documentinternal structures.In this paper, we propose NeuralDater, a Graph Convolutional Network (GCN) based document dating approach which jointly exploits syntactic and temporal graph structures of document in a principled way.To the best of our knowledge, this is the first application of deep learning for the problem of document dating.Through extensive experiments on real-world datasets, we find that NeuralDater significantly outperforms state-of-the-art baseline by 19% absolute (45% relative) accuracy points.

Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods.Relational facts in KG often show temporal dynamics, e.g., the fact (Cristiano Ronaldo, playsFor, Manchester United) is valid only from 2003 to 2009.Most of the existing KG embedding methods ignore this temporal dimension while learning embeddings of the KG elements.In this paper, we propose HyTE, a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane.HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations.Through extensive experimentation on temporal datasets extracted from real-world KGs, we demonstrate the effectiveness of our model over both traditional as well as temporal KG embedding methods.

Knowledge of the creation date of documents facilitates several tasks such as summarization, event extraction, temporally focused information extraction etc.Unfortunately, for most of the documents on the Web, the time-stamp metadata is either missing or can't be trusted.Thus, predicting creation time from document content itself is an important task.In this paper, we propose Attentive Deep Document Dater (AD3), an attention-based neural document dating system which utilizes both context and temporal information in documents in a flexible and principled manner.We perform extensive experimentation on multiple real-world datasets to demonstrate the effectiveness of AD3 over neural and non-neural baselines.