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Detecting an Out-of-Domain (OOD) utterance is crucial for a robust dialog system. Most dialog systems are trained on a pool of annotated OOD data to achieve this goal. However, collecting the annotated OOD data for a given domain is an expensive process. To mitigate this issue, previous works have proposed generative adversarial networks (GAN) based models to generate OOD data for a given domain automatically. However, these proposed models do not work directly with the text. They work with the text’s latent space instead, enforcing these models to include components responsible for encoding text into latent space and decoding it back, such as auto-encoder. These components increase the model complexity, making it difficult to train.
We propose OodGAN, a sequential generative adversarial network (SeqGAN) based model for OOD data generation. Our proposed model works directly on the text and hence eliminates the need to include an auto-encoder. OOD data generated using OodGAN model outperforms state-of-the-art in OOD detection metrics for ROSTD (67% relative improvement in FPR 0.95) and OSQ datasets (28% relative improvement in FPR 0.95) (Zheng et al., 2020).


NAACL 2021

OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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technical paper

We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question. Equipped with data augmentation and multitasking techniques, we show that the proposed framework outperforms the previous SoTA on the CCKS CKBQA dataset. Moreover, we develop a novel data annotation strategy that facilitates the node-to-mention alignment, a dataset (https://github.com/ridiculouz/CKBQA) with such strategy is also published to promote further research. An online demo of NAMER (http://kbqademo.gstore.cn) is provided to visualize our framework and supply extra information for users.

NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering

Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently. Large noisy document collections like the web or news articles make the task more difficult. In this paper, we describe the components of a three-stage automated fact-checking system, named Quin+. We demonstrate that using dense passage representations increases the evidence recall in a noisy setting. We experiment with two sentence selection approaches, an embedding-based selection using a dense retrieval model, and a sequence labeling approach for context-aware selection. Quin+ is able to verify open-domain claims using a large-scale corpus or web search results.


Improving Evidence Retrieval for Automated Explainable Fact-Checking

We introduce Plotting Agent, a system that enables users to directly manipulate plots using natural language instructions within an interactive programming environment. It maps language to plot updates. We formulate this problem as a slot-based task-oriented dialog problem, which we tackle with a sequence-to-sequence model. Practitioners such as data scientists broadly defined, and researchers interested in Natural Language Interfaces (NLIs) may find our demo useful.


Interactive Plot Manipulation using Natural Language

This work demonstrates the development process of a machine learning architecture for inference that can scale to a large volume of requests. In our experiments, we used a BERT model that was fine-tuned for emotion analysis, returning a probability distribution of emotions given a paragraph. The model was deployed as a gRPC service on Kubernetes. Apache Spark was used to perform inference in batches by calling the service. We encountered some performance and concurrency challenges and created solutions to achieve faster running time. Starting with 3.3 successful inference requests per second, we were able to achieve as high as 300 successful requests per second with the same batch job resource allocation. As a result, we successfully stored emotion probabilities for 95 million paragraphs within 96 hours.


An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models

Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.


Noise Robust Named Entity Understanding for Voice Assistants

Named entity linking (NEL) or mapping ``strings" to ``things" in a knowledge base is a fundamental preprocessing step in systems that require knowledge of entities such as information extraction and question answering. In this work, we lay out and investigate two challenges faced by individuals or organizations building NEL systems. Can they directly use an off-the-shelf system? If not, how easily can such a system be repurposed for their use case? First, we conduct a study of off-the-shelf commercial and academic NEL systems. We find that most systems struggle to link rare entities, with commercial solutions lagging their academic counterparts by 10%+. Second, for a use case where the NEL model is used in a sports question-answering (QA) system, we investigate how to close the loop in our analysis by repurposing the best off-the-shelf model (Bootleg) to correct sport-related errors. We show how tailoring a simple technique for patching models using weak labeling can provide a 25% absolute improvement in accuracy of sport-related errors.


Goodwill Hunting: Analyzing and Repurposing Off-the-Shelf Named Entity Linking Systems

Complex natural language understanding modules in dialog systems have a richer understanding of user utterances, and thus are critical in providing a better user experience. However, these models are often created from scratch, for specific clients and use cases, and require the annotation of large datasets. This encourages the sharing of annotated data across multiple clients. To facilitate this we introduce the idea of intent features: domain and topic agnostic properties of intents that can be learned from the syntactic cues only, and hence can be shared. We introduce a new neural network architecture, the Global-Local model, that shows significant improvement over strong baselines for identifying these features in a deployed, multi-intent natural language understanding module, and, more generally, in a classification setting where a part of an utterance has to be classified utilizing the whole context.


Intent Features for Rich Natural Language Understanding

Contract Understanding is an important task of enterprises towards their compliance, audit, revenue and other business goals, given most of their processes are governed by contracts. This presentation describes a Contract Understanding system from IBM that translates this task as solving a series of NLP problems, using a transparent model design enabled by the contept of 'lingustic logical expressions' (LLEs). LLEs enable development teams in IBMs to systematically improve such transparent models with important guarantees of stability and robustness as well as manage changes to said models in a modular fashion. 


Development of an Enterprise-Grade Contract Understanding System

In this study we demonstrate the viability of deploying BERT-style models to serverless environments in a production setting. Since the freely available pre-trained models are too large to be deployed in this way, we utilize knowledge distillation and fine-tune the models on proprietary datasets for two real-world tasks: sentiment analysis and semantic textual similarity. As a result, we obtain models that are tuned for a specific domain and deployable in serverless environments. The subsequent performance analysis shows that this solution results in latency levels acceptable for production use and that it is also a cost-effective approach for small-to-medium size deployments of BERT models, all without any infrastructure overhead.


Cost-effective Deployment of BERT Models in Serverless Environment

Building automatic technical support system is an important yet challenge task. Conceptually, to answer a user question on a technical forum, a human expert has to first retrieve relevant documents, and then read them carefully to identify the answer snippet. Despite huge success the researchers have achieved in coping with general domain question answering (QA), much less attentions have been paid for investigating technical QA. Specifically, existing methods suffer from several unique challenges (i) the question and answer rarely overlaps substantially and (ii) very limited data size. In this paper, we propose a novel framework of deep transfer learning to effectively address technical QA across tasks and domains. To this end, we present an adjustable joint learning approach for document retrieval and reading comprehension tasks. Our experiments on the TechQA demonstrates superior performance compared with state-of-the-art methods.


OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

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OodGAN: Generative Adversarial Network for Out-of-Domain Data Generation

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NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering

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How (Non-)Optimal is the Lexicon?

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