United States

Walk through Challenges of intent detection iin real world setting and performance benchmark of open-source solutions and commercial solutions.


NAACL 2021

Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations

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

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

To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and related biological functions. We have developed a novel and comprehensive knowledge discovery framework, COVID-KG to extract fine-grained multimedia knowledge elements (entities and their visual chemical structures, relations, and events) from the scientific literature. We then exploit the constructed multimedia knowledge graphs (KGs) for question answering and report generation, using drug repurposing as a case study. Our framework also provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence.


COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation

Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https: //textessence.github.io.


TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora

We present a tool that provides automated feedback to students studying Spanish writing. The feedback is given for four categories: topic development, coherence, writing conventions, and essay organization. The tool is made freely available via a Google Docs add-on. A small user study with post-secondary level students in Mexico shows that students found the tool generally helpful and that most of them plan to continue using it as they work to improve their writing skills. In an analysis of 6 months of user data, we see that a small number of users continue to engage with the app, even outside of planned user studies.


Supporting Spanish Writers using Automated Feedback

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.


Technical Question Answering across Tasks and Domains

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).


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

In developing an online question-answering system for the medical domains, natural language inference (NLI) models play a central role in question matching and intention detection. However, which models are best for our datasets? Manually selecting or tuning a model is time-consuming. Thus we experiment with automatically optimizing the model architectures on the task at hand via neural architecture search (NAS). First, we formulate a novel architecture search space based on the previous NAS literature, supporting cross-sentence attention (cross-attn) modeling. Second, we propose to modify the ENAS method to accelerate and stabilize the search results. We conduct extensive experiments on our two medical NLI tasks. Results show that our system can easily outperform the classical baseline models. We compare different NAS methods and demonstrate that our approach provides the best results.


Discovering Better Model Architectures for Medical Query Understanding

Neural models for text generation are often designed in an end-to-end fashion, typically with zero control over intermediate computations, limiting their practical usability in downstream applications. In this work, we incorporate explicit means into neural models to ensure topical continuity, content comprehensiveness and informativeness of automatically generated radiology reports. We propose a
method to compute image representations specific to each sentential context to minimize hallucination caused by sequence-to-sequence approaches and to further eliminate redundant content by exploiting diverse sentence states.
We conduct experiments to generate radiology reports from medical images of chest x-rays using MIMIC-CXR. Our model outperforms baselines by up to 18% and 29% respective in the evaluation for informativeness and content ordering respectively on objective metrics and 16% on human validation


Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations

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