United States

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.


NAACL 2021

Improving Evidence Retrieval for Automated Explainable Fact-Checking

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

Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment. Once deployed, emergent errors can be hard to identify in prediction runtime and impossible to trace back to their sources. To address such gaps, in this paper we propose an error detection framework for sentiment analysis based on explainable features. We perform global-level feature validation with human-in-the-loop assessment, followed by an integration of global and locallevel feature contribution analysis. Experimental results show that, given limited human-inthe- loop intervention, our method is able to identify erroneous model predictions on unseen data with high precision.


When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis

In this presentation, we considered the problem of scaling automated suggested replies for Outlook email system to multiple languages. Faced with increased compute requirements and low resource languages for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system. Restricted data movement across regional clusters prevents joint training across languages. Thus, we proposed a multi-task continual learning framework with auxiliary tasks and language adapters. The results show positive cross-lingual transfer across languages and significant gains in CTR and char-saved online metrics. 


Language Scaling for Universal Suggested Replies Model

Product search is an important problem for retailers to solve as it is a deciding factor to drive business revenues. However, unlike web search, accurate word embeddings for product queries in mid-sized shops are especially difficult to obtain, considering the challenges posed by query length, data sparsity and limited computational capacity. In this talk, we present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products, where the mapping is learned directly from noisy behavioral data. Our experiments show that our model is sample- and cost-efficient, and more accurate than known embedding techniques from the recent NLP and IR literature. Finally, by proposing Query2Prod2Vec, we hope to raise awareness on the interesting challenges of doing NLP inference at “reasonable scale”, and to showcase how research can produce practical solutions that are beneficial for practitioners even outside a few retail giants.


Query2Prod2Vec: Grounded Word Embeddings for eCommerce

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.


Improving Evidence Retrieval for Automated Explainable Fact-Checking

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

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