Simon Corston-Oliver

Also published as: Simon H. Corston-Oliver


Developing a Production System for Purpose of Call Detection in Business Phone Conversations
Elena Khasanova | Pooja Hiranandani | Shayna Gardiner | Cheng Chen | Simon Corston-Oliver | Xue-Yong Fu
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track

For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time. We present a detailed analysis of types of Purpose of Call statements and language patterns related to them, discuss an approach to collect rich training data by bootstrapping from a set of rules to a neural model, and describe a hybrid model which consists of a transformer-based classifier and a set of rules by leveraging insights from the analysis of call transcripts. The model achieved 88.6 F1 on average in various types of business calls when tested on real life data and has low inference time. We reflect on the challenges and design decisions when developing and deploying the system.

BLINK with Elasticsearch for Efficient Entity Linking in Business Conversations
Md Tahmid Rahman Laskar | Cheng Chen | Aliaksandr Martsinovich | Jonathan Johnston | Xue-Yong Fu | Shashi Bhushan Tn | Simon Corston-Oliver
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track

An Entity Linking system aligns the textual mentions of entities in a text to their corresponding entries in a knowledge base. However, deploying a neural entity linking system for efficient real-time inference in production environments is a challenging task. In this work, we present a neural entity linking system that connects the product and organization type entities in business conversations to their corresponding Wikipedia and Wikidata entries. The proposed system leverages Elasticsearch to ensure inference efficiency when deployed in a resource limited cloud machine, and obtains significant improvements in terms of inference speed and memory consumption while retaining high accuracy.

Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning
Xiliang Zhu | Shayna Gardiner | David Rossouw | Tere Roldán | Simon Corston-Oliver
Proceedings of the Third Workshop on Deep Learning for Low-Resource Natural Language Processing

Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability. In addition, building a punctuation restoration system is challenging for low-resource languages, especially for domain-specific applications. In this paper, we propose a Spanish punctuation restoration system designed for a real-time customer support transcription service. To address the data sparsity of Spanish transcripts in the customer support domain, we introduce two transferlearning-based strategies: 1) domain adaptation using out-of-domain Spanish text data; 2) crosslingual transfer learning leveraging in-domain English transcript data. Our experiment results show that these strategies improve the accuracy of the Spanish punctuation restoration system.

An Effective, Performant Named Entity Recognition System for Noisy Business Telephone Conversation Transcripts
Xue-Yong Fu | Cheng Chen | Md Tahmid Rahman Laskar | Shashi Bhushan Tn | Simon Corston-Oliver
Proceedings of the Eighth Workshop on Noisy User-generated Text (W-NUT 2022)

We present a simple yet effective method to train a named entity recognition (NER) model that operates on business telephone conversation transcripts that contain noise due to the nature of spoken conversation and artifacts of automatic speech recognition. We first fine-tune LUKE, a state-of-the-art Named Entity Recognition (NER) model, on a limited amount of transcripts, then use it as the teacher model to teach a smaller DistilBERT-based student model using a large amount of weakly labeled data and a small amount of human-annotated data. The model achieves high accuracy while also satisfying the practical constraints for inclusion in a commercial telephony product: realtime performance when deployed on cost-effective CPUs rather than GPUs. In this paper, we introduce the fine-tune-then-distill method for entity recognition on real world noisy data to deploy our NER model in a limited budget production environment. By generating pseudo-labels using a large teacher model pre-trained on typed text while fine-tuned on noisy speech text to train a smaller student model, we make the student model 75x times faster while reserving 99.09% of its accuracy. These findings demonstrate that our proposed approach is very effective in limited budget scenarios to alleviate the need of human labeling of a large amount of noisy data.


Improving Punctuation Restoration for Speech Transcripts via External Data
Xue-Yong Fu | Cheng Chen | Md Tahmid Rahman Laskar | Shashi Bhushan | Simon Corston-Oliver
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

Automatic Speech Recognition (ASR) systems generally do not produce punctuated transcripts. To make transcripts more readable and follow the expected input format for downstream language models, it is necessary to add punctuation marks. In this paper, we tackle the punctuation restoration problem specifically for the noisy text (e.g., phone conversation scenarios). To leverage the available written text datasets, we introduce a data sampling technique based on an n-gram language model to sample more training data that are similar to our in-domain data. Moreover, we propose a two-stage fine-tuning approach that utilizes the sampled external data as well as our in-domain dataset for models based on BERT. Extensive experiments show that the proposed approach outperforms the baseline with an improvement of 1.12% F1 score.


The impact of parse quality on syntactically-informed statistical machine translation
Chris Quirk | Simon Corston-Oliver
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

Dependency Parsing with Reference to Slovene, Spanish and Swedish
Simon Corston-Oliver | Anthony Aue
Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X)

Multilingual Dependency Parsing using Bayes Point Machines
Simon Corston-Oliver | Anthony Aue | Kevin Duh | Eric Ringger
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference


Normalizing German and English inflectional morphology to improve statistical word alignment
Simon Corston-Oliver | Michael Gamon
Proceedings of the 6th Conference of the Association for Machine Translation in the Americas: Technical Papers

German has a richer system of inflectional morphology than English, which causes problems for current approaches to statistical word alignment. Using Giza++ as a reference implementation of the IBM Model 1, an HMMbased alignment and IBM Model 4, we measure the impact of normalizing inflectional morphology on German-English statistical word alignment. We demonstrate that normalizing inflectional morphology improves the perplexity of models and reduces alignment errors.

Linguistically Informed Statistical Models of Constituent Structure for Ordering in Sentence Realization
Eric Ringger | Michael Gamon | Robert C. Moore | David Rojas | Martine Smets | Simon Corston-Oliver
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

Task-Focused Summarization of Email
Simon Corston-Oliver | Eric Ringger | Michael Gamon | Richard Campbell
Text Summarization Branches Out


French Amalgam: a quick adaptation of a sentence realization system to French
Martine Smets | Michael Gamon | Simon Corston-Oliver | Eric Ringger
10th Conference of the European Chapter of the Association for Computational Linguistics

French Amalgam: A machine-learned sentence realization system
Martine Smets | Michael Gamon | Simon Corston-Oliver | Eric Ringger
Actes de la 10ème conférence sur le Traitement Automatique des Langues Naturelles. Articles longs

This paper presents the French implementation of Amalgam, a machine-learned sentence realization system. It presents in some detail two of the machine-learned models employed in Amalgam and shows how linguistic intuition and knowledge can be combined with statistical techniques to improve the performance of the models.

Combining decision trees and transformation-based learning to correct transferred linguistic representations
Simon Corston-Oliver | Michael Gamon
Proceedings of Machine Translation Summit IX: Papers

We approach to correcting features in transferred linguistic representations in machine translation. The hybrid approach combines decision trees and transformation-based learning. Decision trees serve as a filter on the intractably large search space of possible interrelations among features. Transformation-based learning results in a simple set of ordered rules that can be compiled and executed after transfer and before sentence realization in the target language. We measure the reduction in noise in the linguistic representations and the results of human evaluations of end-to-end English-German machine translation.


Machine-learned contexts for linguistic operations in German sentence realization
Michael Gamon | Eric Ringger | Simon Corston-Oliver | Robert Moore
Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics

Extraposition: A Case Study in German Sentence Realization
Michael Gamon | Eric Ringger | Zhu Zhang | Robert Moore | Simon Corston-Oliver
COLING 2002: The 19th International Conference on Computational Linguistics

An Overview of Amalgam: A Machine-learned Generation Module
Simon Corston-Oliver | Michael Gamon | Eric Ringger | Robert Moore
Proceedings of the International Natural Language Generation Conference


A Machine Learning Approach to the Automatic Evaluation of Machine Translation
Simon Corston-Oliver | Michael Gamon | Chris Brockett
Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics

Using machine learning for system-internal evaluation of transferred linguistic representations
Michael Gamon | Hisami Suzuki | Simon Corston-Oliver
Proceedings of Machine Translation Summit VIII

We present an automated, system-internal evaluation technique for linguistic representations in a large-scale, multilingual MT system. We use machine-learned classifiers to recognize the differences between linguistic representations generated from transfer in an MT context from representations that are produced by "native" analysis of the target language. In the MT scenario, convergence of the two is the desired result. Holding the feature set and the learning algorithm constant, the accuracy of the classifiers provides a measure of the overall difference between the two sets of linguistic representations: classifiers with higher accuracy correspond to more pronounced differences between representations. More importantly, the classifiers yield the basis for error-analysis by providing a ranking of the importance of linguistic features. The more salient a linguistic criterion is in discriminating transferred representations from "native" representations, the more work will be needed in order to get closer to the goal of producing native-like MT. We present results from using this approach on the Microsoft MT system and discuss its advantages and possible extensions.


Using decision trees to select the grammatical relation of a noun phrase
Simon Corston-Oliver
1st SIGdial Workshop on Discourse and Dialogue

Book Reviews: Natural Language Information Retrieval
Simon Corston-Oliver
Computational Linguistics, Volume 26, Number 3, September 2000


Less is more: Eliminating index terms from subordinate clauses
Simon H. Corston-Oliver | William B. Dolan
Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics


pdf bib
Identifying the Linguistic Correlates of Rhetorical Relations
Simon H. Corston-Oliver
Discourse Relations and Discourse Markers