Proceedings of the 2018 Conference of the North
Scalable Wide and Deep Learning for Computer Assisted Coding (N18-3)
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Marilisa Amoia, Frank Diehl, Jesus Gimenez, Joel Pinto, Raphael Schumann, Fabian Stemmer, Paul Vozila, Yi Zhang
| Challenge: | In recent years the use of electronic medical records has accelerated resulting in large volumes of medical data when a patient visits a healthcare facility. |
| Approach: | They propose to use convolutional neural networks and logistic regression to build a machine learning based system for predicting ICD-10 codes from electronic medical records. |
| Outcome: | The proposed system can predict ICD-10 codes from electronic medical records using convolutional neural networks and logistic regression models. |
Neural Network based Extreme Classification and Similarity Models for Product Matching (N18-3)
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| Challenge: | Matching a seller listed item to an appropriate product has become a fundamental step for e-commerce platforms. |
| Approach: | They propose to use a shallow neural network to match a seller's item to an appropriate product . they also propose a similarity approach based on deep siamese network to train and infer product information. |
| Outcome: | The proposed models outperform the baseline models by more than 5% in terms of accuracy and are capable of efficient training and inference. |
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding (N18-3)
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| Challenge: | Existing approaches to classify a given utterance into domains are costly and time-consuming. |
| Approach: | They propose a shortlisting-reranking neural model for large-scale domain classification for IPDAs . they use extensive experiments on 1,500 IPDA domains to test their effectiveness . |
| Outcome: | The proposed model is tested on 1,500 IPDA domains. |
What we need to learn if we want to do and not just talk (N18-3)
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| Challenge: | Existing methods for task-oriented dialogs require fluent natural language responses and correct external actions . but they perform poorly in real world dialog tasks, a new study shows . |
| Approach: | They propose a hybrid model where nearest neighbor is used to generate fluent responses and Seq2Seq type models ensure dialogue coherency and generate accurate external actions. |
| Outcome: | The proposed model achieves a 78% relative improvement in fluency and 200% improvement in accuracy of external calls. |
Data Collection for Dialogue System: A Startup Perspective (N18-3)
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| Challenge: | Developing dialogue systems such as Apple Siri and Google Now requires high quality training data but data collection with crowdsourcing is largely an open question. |
| Approach: | They propose to use crowdsourcing to collect data for a user intent classification task in a dialogue system. |
| Outcome: | The proposed method improves the quality of the collected data and the model performance on real user queries. |
Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning (N18-3)
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| Challenge: | End-to-end neural models for conversational agents require large corpus of dialogues to learn effectively. |
| Approach: | They propose a method for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing. |
| Outcome: | The proposed approach can be quickly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users. |
Quality Estimation for Automatically Generated Titles of eCommerce Browse Pages (N18-3)
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| Challenge: | We are generating millions of titles using machine translation, but they are prone to errors. |
| Approach: | They propose a Random Forest model which explores hand-crafted features and new features . they also propose SNs which embed metadata and generated title in the same space . |
| Outcome: | The proposed models outperform the existing models on in-house data. |
Atypical Inputs in Educational Applications (N18-3)
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| Challenge: | atypical characteristics of some responses make it difficult for an automated scoring system to assign a valid score . a typical spoken response with a lot of background noise may suffer from frequent errors in automated speech recognition . |
| Approach: | They propose a pipeline that detects and processes non-scorable responses at run-time . they also propose linguistic filtering models for spoken responses in language tests . |
| Outcome: | The proposed pipeline detects and processes non-scorable responses at run-time and evaluates them for spoken responses in language proficiency assessment. |
Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles (N18-3)
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| Challenge: | Existing work on aspect extraction from reviews has focused on capturing aspects of user preferences. |
| Approach: | They propose a neural model for aspect extraction from reviews . they use a k-means baseline to extract canonical sentences of various aspects from reviews. |
| Outcome: | The proposed model performs well on two tasks. |
SystemT: Declarative Text Understanding for Enterprise (N18-3)
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| Challenge: | a growing number of enterprise applications are relying on text understanding systems to understand information in unstructured and semi-structured forms. |
| Approach: | They propose a declarative text understanding system that addresses these challenges . they summarize the impact of SystemT on business and education . |
| Outcome: | The system addresses the challenges of enterprise text understanding systems . it has been deployed in a wide range of enterprise applications . |
Construction of the Literature Graph in Semantic Scholar (N18-3)
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Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew Peters, Joanna Power, Sam Skjonsberg, Lucy Lu Wang, Chris Wilhelm, Zheng Yuan, Madeleine van Zuylen, Oren Etzioni
| Challenge: | Fig. 1 summarizes a scalable system for organizing published scientific literature into a heterogeneous graph . authors describe methods used to enable semantic features in www.semanticscholar.org . |
| Approach: | They describe a scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. |
| Outcome: | The proposed system can be deployed on a scalable platform and report empirical results for each task. |
Can Neural Machine Translation be Improved with User Feedback? (N18-3)
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| Challenge: | a recent study has focused on the use of explicit and implicit feedback for neural machine translation (NMT) a new study uses explicit and implied feedback to improve performance of NMT with human reinforcement. |
| Approach: | They propose to use real logged feedback to improve neural machine translation with human reinforcement. |
| Outcome: | The proposed method improves translation quality metrics with implicit task-based feedback . the proposed method is based on explicit and implicit feedback collected on the eBay platform . |
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment (N18-3)
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| Challenge: | LMBR techniques for NMT still yield better results than Transformers . but with NMT, real time decoding is challenging without GPUs and high-end GPUs are expensive. |
| Approach: | They propose a batched beam decoding algorithm for NMT with LMBR n-gram posteriors and an acceleration strategy for deployment to take advantage of the higher adequacy. |
| Outcome: | The proposed method outperforms the most recent results with Transformers in terms of speed and memory usage. |
Pieces of Eight: 8-bit Neural Machine Translation (N18-3)
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| Challenge: | Neural machine translation models are trained using 32-bit floating point values and have improved fluency and adequacy. |
| Approach: | They propose to use 8-bit quantization to train models using 32-bit floating point values and show that 8- bit translation makes a non-negligible impact in terms of speed with no degradation in accuracy and adequacy. |
| Outcome: | The proposed method improves accuracy and accuracy without degradation in accuracy and adequacy. |
From dictations to clinical reports using machine translation (N18-3)
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Gregory Finley, Wael Salloum, Najmeh Sadoughi, Erik Edwards, Amanda Robinson, Nico Axtmann, Michael Brenndoerfer, Mark Miller, David Suendermann-Oeft
| Challenge: | Medical dictation is one of the most common ways to document clinical encounters. |
| Approach: | They propose a machine callytranslation technique that automates post-processing tasks . they show that it outperforms conventional systems in correcting errors . |
| Outcome: | The proposed method outperforms conventional systems in many tasks while being much simpler to maintain. |
Benchmarks and models for entity-oriented polarity detection (N18-3)
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| Challenge: | a dataset of 17,000 manually labeled documents is large for determining entity-oriented polarity in business news. |
| Approach: | They propose a convolutional neural network-based approach to classify entity-oriented polarity in business news. |
| Outcome: | The proposed model is based on convolutional neural networks and is small on the scale of existing models. |
Selecting Machine-Translated Data for Quick Bootstrapping of a Natural Language Understanding System (N18-3)
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| Challenge: | In recent years, there has been growing interest in voice-controlled devices, such as Amazon Alexa or Google home. |
| Approach: | They investigate the use of Machine Translation to bootstrap a natural language understanding system for a new language for the use case of a large-scale voice-controlled device. |
| Outcome: | The proposed method reduces the time and cost of getting annotated corpus for a new language while still providing a large enough coverage of user requests. |
Fast and Scalable Expansion of Natural Language Understanding Functionality for Intelligent Agents (N18-3)
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| Challenge: | a recent paper describes efficient deep neural network architectures for expanding natural language capabilities of virtual agents. |
| Approach: | They propose deep neural network architectures that maximize re-use available resources . they use data from Amazon Alexa to accelerate expansion of new natural language domains . |
| Outcome: | The proposed methods increase accuracy in low resource settings and enable rapid development with less data. |
Bag of Experts Architectures for Model Reuse in Conversational Language Understanding (N18-3)
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| Challenge: | Slot tagging is a key component of natural language understanding systems for personal digital assistants. |
| Approach: | They propose to use a bag of experts architecture to reuse domain data for slot tagging models. |
| Outcome: | Experiments with 10 domains show that the proposed models outperform baseline models by 5.06% and 12.16% when training with only 25% of the training data. |
Multi-lingual neural title generation for e-Commerce browse pages (N18-3)
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| Challenge: | e-Commerce websites are automatically generating millions of browse pages . manual creation of titles is infeasible due to the huge number of browse page types . |
| Approach: | They propose to use sequence-to-sequence models to generate titles for languages . they train the models on multi-lingual data, thereby creating one joint model . |
| Outcome: | The proposed model can generate titles in three different languages, with a focus on low-resource French. |
A Novel Approach to Part Name Discovery in Noisy Text (N18-3)
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| Challenge: | Part names are often multi-word terms longer than two words, and there is little consistency in how terms are described in noisy free text. |
| Approach: | They propose an algorithm that exploits statistical, linguistic and machine learning techniques to discover part names in noisy text. |
| Outcome: | The proposed method outperforms existing methods significantly in part name extraction. |
The Alexa Meaning Representation Language (N18-3)
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Thomas Kollar, Danielle Berry, Lauren Stuart, Karolina Owczarzak, Tagyoung Chung, Lambert Mathias, Michael Kayser, Bradford Snow, Spyros Matsoukas
| Challenge: | a new meaning representation language for spoken language is introduced for Alexa . AMRL provides a common representation for how people communicate in spoken language . there is no mechanism to represent ambiguity, forcing the choice of a fixed interpretation for ambiguous utterances. |
| Approach: | They introduce a meaning representation for spoken language, the Alexa meaning represent language . they use a spoken language dataset to collect a sample of utterances from eight domains . |
| Outcome: | The proposed representation provides a common representation for spoken language understanding . it supports cross-domain queries, fine-grained types, complex utterances and composition . the proposed representation was released to developers at a trade show in 2016 . |
Practical Application of Domain Dependent Confidence Measurement for Spoken Language Understanding Systems (N18-3)
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| Challenge: | a confidence score is a scalar quantity that measures the reliability of an automatic system. |
| Approach: | They propose to use a confidence measure to evaluate the reliability of an SLU system . they build confidence models for three different types of dialogue states . |
| Outcome: | The proposed model can be used to reject low-confidence SLU results in real-world scenarios. |
Prediction for the Newsroom: Which Articles Will Get the Most Comments? (N18-3)
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| Challenge: | a new method to support manual moderation of discussion sections is proposed. |
| Approach: | They propose to support manual moderation by proactively drawing attention of moderators to articles that most likely need their intervention. |
| Outcome: | The proposed method outperforms the current state-of-the-art methods on a 7-million-comment dataset. |
Demand-Weighted Completeness Prediction for a Knowledge Base (N18-3)
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| Challenge: | Knowledge Bases (KBs) are widely used for representing information in a structured format. |
| Approach: | They propose a method to measure Demand-Weighted Completeness by defining an entity by its classes and using usage data to predict relation distributions. |
| Outcome: | The proposed method can be used to estimate completeness of knowledge bases based on how they are used and can quantify usage and completeness changes over time. |
Personalized neural language models for real-world query auto completion (N18-3)
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| Challenge: | Existing popularity-based methods for query auto completion (QAC) are ineffective in predicting unseen queries. |
| Approach: | They propose to use real-world data to build an end-to-end system that can predict unseen queries by integrating user information. |
| Outcome: | The proposed methods improve on two separate datasets while increasing diversity while scalability. |
Document-based Recommender System for Job Postings using Dense Representations (N18-3)
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| Challenge: | 45% of job posting traffic is driven by recommender systems for job postings . a large-scale job recommendation system is needed to detect similarity between job posting and item-to-item based recommendations. |
| Approach: | They propose to use dense vector representations to enhance a large-scale job recommendation system and rank job advertisements regarding similarity. |
| Outcome: | The proposed method increases the click-through rate on job recommendations by 8.0%. |