Shahab Jalalvand


A Hybrid Approach to Scalable and Robust Spoken Language Understanding in Enterprise Virtual Agents
Ryan Price | Mahnoosh Mehrabani | Narendra Gupta | Yeon-Jun Kim | Shahab Jalalvand | Minhua Chen | Yanjie Zhao | Srinivas Bangalore
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Spoken language understanding (SLU) extracts the intended mean- ing from a user utterance and is a critical component of conversational virtual agents. In enterprise virtual agents (EVAs), language understanding is substantially challenging. First, the users are infrequent callers who are unfamiliar with the expectations of a pre-designed conversation flow. Second, the users are paying customers of an enterprise who demand a reliable, consistent and efficient user experience when resolving their issues. In this work, we describe a general and robust framework for intent and entity extraction utilizing a hybrid of statistical and rule-based approaches. Our framework includes confidence modeling that incorporates information from all components in the SLU pipeline, a critical addition for EVAs to en- sure accuracy. Our focus is on creating accurate and scalable SLU that can be deployed rapidly for a large class of EVA applications with little need for human intervention.


FBK’s Neural Machine Translation Systems for IWSLT 2016
M. Amin Farajian | Rajen Chatterjee | Costanza Conforti | Shahab Jalalvand | Vevake Balaraman | Mattia A. Di Gangi | Duygu Ataman | Marco Turchi | Matteo Negri | Marcello Federico
Proceedings of the 13th International Conference on Spoken Language Translation

In this paper, we describe FBK’s neural machine translation (NMT) systems submitted at the International Workshop on Spoken Language Translation (IWSLT) 2016. The systems are based on the state-of-the-art NMT architecture that is equipped with a bi-directional encoder and an attention mechanism in the decoder. They leverage linguistic information such as lemmas and part-of-speech tags of the source words in the form of additional factors along with the words. We compare performances of word and subword NMT systems along with different optimizers. Further, we explore different ensemble techniques to leverage multiple models within the same and across different networks. Several reranking methods are also explored. Our submissions cover all directions of the MSLT task, as well as en-{de, fr} and {de, fr}-en directions of TED. Compared to previously published best results on the TED 2014 test set, our models achieve comparable results on en-de and surpass them on en-fr (+2 BLEU) and fr-en (+7.7 BLEU) language pairs.

TranscRater: a Tool for Automatic Speech Recognition Quality Estimation
Shahab Jalalvand | Matteo Negri | Marco Turchi | José G. C. de Souza | Daniele Falavigna | Mohammed R. H. Qwaider
Proceedings of ACL-2016 System Demonstrations


Driving ROVER with Segment-based ASR Quality Estimation
Shahab Jalalvand | Matteo Negri | Daniele Falavigna | Marco Turchi
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


Parameter optimization for iterative confusion network decoding in weather-domain speech recognition
Shahab Jalalvand | Daniele Falavigna
Proceedings of the 10th International Workshop on Spoken Language Translation: Papers

In this paper, we apply a set of approaches to, efficiently, rescore the output of the automatic speech recognition over weather-domain data. Since the in-domain data is usually insufficient for training an accurate language model (LM) we utilize an automatic selection method to extract domain-related sentences from a general text resource. Then, an N-gram language model is trained on this set. We exploit this LM, along with a pre-trained acoustic model for recognition of the development and test instances. The recognizer generates a confusion network (CN) for each instance. Afterwards, we make use of the recurrent neural network language model (RNNLM), trained on the in-domain data, in order to iteratively rescore the CNs. Rescoring the CNs, in this way, requires estimating the weights of the RNNLM, N-gramLM and acoustic model scores. Weights optimization is the critical part of this work, whereby, we propose using the minimum error rate training (MERT) algorithm along with a novel N-best list extraction method. The experiments are done over weather forecast domain data that has been provided in the framework of EUBRIDGE project.

Improving Language Model Adaptation using Automatic Data Selection and Neural Network
Shahab Jalalvand
Proceedings of the Student Research Workshop associated with RANLP 2013