Kevin Leach


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On Evaluation of Document Classification with RVL-CDIP
Stefan Larson | Gordon Lim | Kevin Leach
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

The RVL-CDIP benchmark is widely used for measuring performance on the task of document classification. Despite its widespread use, we reveal several undesirable characteristics of the RVL-CDIP benchmark. These include (1) substantial amounts of label noise, which we estimate to be 8.1% (ranging between 1.6% to 16.9% per document category); (2) presence of many ambiguous or multi-label documents; (3) a large overlap between test and train splits, which can inflate model performance metrics; and (4) presence of sensitive personally-identifiable information like US Social Security numbers (SSNs). We argue that there is a risk in using RVL-CDIP for benchmarking document classifiers, as its limited scope, presence of errors (state-of-the-art models now achieve accuracy error rates that are within our estimated label error rate), and lack of diversity make it less than ideal for benchmarking. We further advocate for the creation of a new document classification benchmark, and provide recommendations for what characteristics such a resource should include.


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One Agent To Rule Them All: Towards Multi-agent Conversational AI
Christopher Clarke | Joseph Peper | Karthik Krishnamurthy | Walter Talamonti | Kevin Leach | Walter Lasecki | Yiping Kang | Lingjia Tang | Jason Mars
Findings of the Association for Computational Linguistics: ACL 2022

The increasing volume of commercially available conversational agents (CAs) on the market has resulted in users being burdened with learning and adopting multiple agents to accomplish their tasks. Though prior work has explored supporting a multitude of domains within the design of a single agent, the interaction experience suffers due to the large action space of desired capabilities. To address these problems, we introduce a new task BBAI: Black-Box Agent Integration, focusing on combining the capabilities of multiple black-box CAs at scale. We explore two techniques: question agent pairing and question response pairing aimed at resolving this task. Leveraging these techniques, we design One For All (OFA), a scalable system that provides a unified interface to interact with multiple CAs. Additionally, we introduce MARS: Multi-Agent Response Selection, a new encoder model for question response pairing that jointly encodes user question and agent response pairs. We demonstrate that OFA is able to automatically and accurately integrate an ensemble of commercially available CAs spanning disparate domains. Specifically, using the MARS encoder we achieve the highest accuracy on our BBAI task, outperforming strong baselines.

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Redwood: Using Collision Detection to Grow a Large-Scale Intent Classification Dataset
Stefan Larson | Kevin Leach
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Dialog systems must be capable of incorporating new skills via updates over time in order to reflect new use cases or deployment scenarios. Similarly, developers of such ML-driven systems need to be able to add new training data to an already-existing dataset to support these new skills. In intent classification systems, problems can arise if training data for a new skill’s intent overlaps semantically with an already-existing intent. We call such cases collisions. This paper introduces the task of intent collision detection between multiple datasets for the purposes of growing a system’s skillset. We introduce several methods for detecting collisions, and evaluate our methods on real datasets that exhibit collisions. To highlight the need for intent collision detection, we show that model performance suffers if new data is added in such a way that does not arbitrate colliding intents. Finally, we use collision detection to construct and benchmark a new dataset, Redwood, which is composed of 451 categories from 13 original intent classification datasets, making it the largest publicly available intent classification benchmark.


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Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data
Stefan Larson | Eric Guldan | Kevin Leach
Proceedings of the Twelfth Language Resources and Evaluation Conference

Typical machine learning approaches to developing task-oriented dialog systems require the collection and management of large amounts of training data, especially for the tasks of intent classification and slot-filling. Managing this data can be cumbersome without dedicated tools to help the dialog system designer understand the nature of the data. This paper presents a toolkit for analyzing slot-filling and intent classification corpora. We present a toolkit that includes (1) a new lightweight and readable data and file format for intent classification and slot-filling corpora, (2) a new query language for searching intent classification and slot-filling corpora, and (3) tools for understanding the structure and makeup for such corpora. We apply our toolkit to several well-known NLU datasets, and demonstrate that our toolkit can be used to uncover interesting and surprising insights. By releasing our toolkit to the research community, we hope to enable others to develop more robust and intelligent slot-filling and intent classification models.

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Inconsistencies in Crowdsourced Slot-Filling Annotations: A Typology and Identification Methods
Stefan Larson | Adrian Cheung | Anish Mahendran | Kevin Leach | Jonathan K. Kummerfeld
Proceedings of the 28th International Conference on Computational Linguistics

Slot-filling models in task-driven dialog systems rely on carefully annotated training data. However, annotations by crowd workers are often inconsistent or contain errors. Simple solutions like manually checking annotations or having multiple workers label each sample are expensive and waste effort on samples that are correct. If we can identify inconsistencies, we can focus effort where it is needed. Toward this end, we define six inconsistency types in slot-filling annotations. Using three new noisy crowd-annotated datasets, we show that a wide range of inconsistencies occur and can impact system performance if not addressed. We then introduce automatic methods of identifying inconsistencies. Experiments on our new datasets show that these methods effectively reveal inconsistencies in data, though there is further scope for improvement.

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Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness
Stefan Larson | Anthony Zheng | Anish Mahendran | Rishi Tekriwal | Adrian Cheung | Eric Guldan | Kevin Leach | Jonathan K. Kummerfeld
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts. We propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing. We show how prior workflows are special cases of our approach, and present a way to apply the approach to dialog tasks such as intent classification and slot-filling. Using our method, we create more challenging versions of test sets from prior dialog datasets and find dramatic performance drops for standard models. Finally, we show that our approach is complementary to recent work on improving data diversity, and training on data collected with our approach leads to more robust models.


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An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
Stefan Larson | Anish Mahendran | Joseph J. Peper | Christopher Clarke | Andrew Lee | Parker Hill | Jonathan K. Kummerfeld | Kevin Leach | Michael A. Laurenzano | Lingjia Tang | Jason Mars
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope—i.e., queries that do not fall into any of the system’s supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.