Proceedings of the 28th International Conference on Computational Linguistics: Industry Track
Evaluating Cross-Lingual Transfer Learning Approaches in Multilingual Conversational Agent Models (2020.coling-industry)
Copied to clipboard
| Challenge: | Existing voice assistant models are developed for each region or language, requiring linear effort to develop and maintain. |
| Approach: | They propose a general multilingual model framework for natural language understanding models . they show multilingual models can reach same or better performance compared to monolingual models a . |
| Outcome: | The proposed model framework can bootstrap new language models faster and reduce effort . it can reach same or better performance compared to monolingual models across language-specific test data . |
Data-Efficient Paraphrase Generation to Bootstrap Intent Classification and Slot Labeling for New Features in Task-Oriented Dialog Systems (2020.coling-industry)
Copied to clipboard
| Challenge: | a number of dialog systems have been developed to perform tasks with high accuracy on benchmarks, but there is a problem with annotated seed data. |
| Approach: | They propose a model that augments initial seed data by paraphrasing existing utterances automatically. |
| Outcome: | The proposed approach improves intent classification and slot labeling on a public dataset and with a real-world dialog system. |
Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances (2020.coling-industry)
Copied to clipboard
| Challenge: | In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives. |
| Approach: | They propose a method to leverage this feedback by creating annotated training examples from it. |
| Outcome: | The proposed method can be used in a commercial dialog system across various domains and three languages. |
Query Distillation: BERT-based Distillation for Ensemble Ranking (2020.coling-industry)
Copied to clipboard
| Challenge: | Recent years have witnessed substantial progress in the development of neural ranking networks, but an increasingly heavy computational burden due to growing numbers of parameters and the adoption of model ensembles. |
| Approach: | They propose a two-stage distillation method that allows a smaller student model to be trained while benefiting from the better performance of the teacher model. |
| Outcome: | The proposed method shows higher-quality rankings compared to the teacher model. |
Semantic Diversity for Natural Language Understanding Evaluation in Dialog Systems (2020.coling-industry)
Copied to clipboard
| Challenge: | a dialog system is used to evaluate NLU models using aggregated metrics on a large number of utterances. |
| Approach: | They propose a method to generate a test set with high semantic diversity for NLU evaluation in dialog systems. |
| Outcome: | The proposed test sets are based on high diversity of utterances from different regions of the utteration embedding space. |
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation (2020.coling-industry)
Copied to clipboard
Pawel Bujnowski, Kseniia Ryzhova, Hyungtak Choi, Katarzyna Witkowska, Jaroslaw Piersa, Tymoteusz Krumholc, Katarzyna Beksa
| Challenge: | a limited amount of style data is needed for text style transfer, but there are no convincing methods for evaluating them. |
| Approach: | They propose an efficient method for neutral-to-style transformation using the transformer framework. |
| Outcome: | The proposed method can train neutral-to-style transformation models using large paraphrases and a small style transfer corpus. |
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data (2020.coling-industry)
Copied to clipboard
Ankit Arun, Soumya Batra, Vikas Bhardwaj, Ashwini Challa, Pinar Donmez, Peyman Heidari, Hakan Inan, Shashank Jain, Anuj Kumar, Shawn Mei, Karthik Mohan, Michael White
| Challenge: | Natural language generation (NLG) is a critical component in conversational systems . Traditionally, NLG components have been deployed using template-based solutions . however, deployment of such model-based systems has been challenging due to high latency and data needs. |
| Approach: | They propose a family of techniques to deploy data-efficient neural solutions for NLG in conversational systems to production. |
| Outcome: | The proposed techniques achieve production quality with light-weight neural network models using fraction of the data needed otherwise. |
Interactive Question Clarification in Dialogue via Reinforcement Learning (2020.coling-industry)
Copied to clipboard
| Challenge: | ambiguous questions are a perennial problem in real-world dialogue systems. |
| Approach: | They propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. |
| Outcome: | The proposed model improves on real-world user clicks and shows significant improvements . it suggests that the original query is refined to clarify ambiguous questions . |
Towards building a Robust Industry-scale Question Answering System (2020.coling-industry)
Copied to clipboard
| Challenge: | Existing systems that use “zero-shot transfer learning” (ZSTL) are difficult to train and have observation biases. |
| Approach: | They propose a production model called GAAMA which has two characteristics . it is robust and efficient, and trains on the recently introduced Natural Questions dataset . |
| Outcome: | The proposed model performs on two benchmarks: BioASQ and CovidQA. |
Delexicalized Paraphrase Generation (2020.coling-industry)
Copied to clipboard
| Challenge: | Using convolutional neural networks, we generate delexicalized sentences . 1.29% accuracy is achieved with the generated paraphrases . |
| Approach: | They propose a neural paraphrasing model that generates delexicalized sentences . they use convolutional neural networks to pool on slot values and use pointers to locate them . |
| Outcome: | The proposed model generates delexicalized sentences with high quality . it can be used for intent classification and named entity recognition tasks . |
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention (2020.coling-industry)
Copied to clipboard
| Challenge: | Existing methods to integrate hypotheses into speech recognition systems are noisy and can cause information loss. |
| Approach: | They propose to integrate hypotheses into multi-task learning and transfer learning to improve performance. |
| Outcome: | The proposed model improves domain and intent classification by 19% and 37% compared to current methods . the proposed model could recover transcription and rewrite the query for a better understanding . |
Misspelling Detection from Noisy Product Images (2020.coling-industry)
Copied to clipboard
| Challenge: | Existing spelling research has focused on advancement in misspelling correction . a single inadvertent or intentional misspeller can propagate to large amounts of inventory . |
| Approach: | They propose a method to automatically detect misspellings from product images . they curate a large corpus and define a rich set of features to validate the method . |
| Outcome: | The proposed method improves on a large corpus and an out-of-domain public dataset and improves by 20% in the F1 score. |
hinglishNorm - A Corpus of Hindi-English Code Mixed Sentences for Text Normalization (2020.coling-industry)
Copied to clipboard
| Challenge: | hinglishNorm is a human annotated corpus of Hindi-English code-mixed sentences for text normalization task. |
| Approach: | They propose to annotate sentences in Hindi-English code-mixed sentences using a human annotated normalized form. |
| Outcome: | The proposed corpus contains 13494 segments annotated for text normalization. |
Assessing Social License to Operate from the Public Discourse on Social Media (2020.coling-industry)
Copied to clipboard
| Challenge: | Social License to Operate (SLO) is the level of support organisations gain from the public. |
| Approach: | They propose to extract and transform peoples’ stances towards an organisation into SLO levels by performing a chain of three text classification tasks. |
| Outcome: | The proposed system extracts and transforms peoples’ stances towards an organisation into SLO levels. |
Extreme Model Compression for On-device Natural Language Understanding (2020.coling-industry)
Copied to clipboard
| Challenge: | Xu and Sarikaya et al., 2014) perform word-embedding compression with NLU task learning . their approach achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance. |
| Approach: | They propose a task-aware, end-to-end compression approach that performs word-embedding compression with NLU task learning. |
| Outcome: | The proposed approach outperforms baselines and achieves 97.4% compression rate with less than 3.7% degradation in predictive performance. |
Scalable Cross-lingual Treebank Synthesis for Improved Production Dependency Parsers (2020.coling-industry)
Copied to clipboard
| Challenge: | scalable Universal Dependency (UD) treebank synthesis techniques are used to improve production-grade parsers. |
| Approach: | They propose a data augmentation technique that uses synthetic treebanks to improve production-grade parsers. |
| Outcome: | The proposed technique improves LAS performance on seven languages by up to two points on production models trained on original UD treebanks. |
An Industry Evaluation of Embedding-based Entity Alignment (2020.coling-industry)
Copied to clipboard
| Challenge: | Knowledge graphs (KGs) are increasingly important in various applications such as question answering and search engines. |
| Approach: | They propose to use a supervised learning environment with unbiased seed mappings for training and validation to evaluate alignment methods in an industrial context. |
| Outcome: | The proposed methods are evaluated in an industrial context and are compared with DBpedia and Wikidata benchmarks. |
Learning Domain Terms - Empirical Methods to Enhance Enterprise Text Analytics Performance (2020.coling-industry)
Copied to clipboard
| Challenge: | Standard text analytics algorithms are degraded on consumer generated data, a study finds . enterprise data contains domain terms that are not available in general purpose thesaurus . |
| Approach: | They propose unsupervised methods for cleaning noisy repositories after domain terms are segregated from true noise terms. |
| Outcome: | The proposed method improves classification accuracy after noise correction . it is based on a systematic analysis of enterprise data . |
Model-agnostic Methods for Text Classification with Inherent Noise (2020.coling-industry)
Copied to clipboard
| Challenge: | Text classification is a fundamental problem in natural language processing, but its performance relies on high-quality annotations. |
| Approach: | They propose to use model-agnostic methods to handle inherent noise in large scale text classification that can be easily incorporated into existing machine learning workflows with minimal interruption. |
| Outcome: | The proposed method outperforms baselines by up to 10% in classification accuracy while requiring no network modifications. |
ScopeIt: Scoping Task Relevant Sentences in Documents (2020.coling-industry)
Copied to clipboard
| Challenge: | a problem faced by conversational agents working with large documents is the frequent presence of information that is irrelevant to the agent. |
| Approach: | They propose a neural model for scoping relevant information from a large document . they show that the model performs better with emails than existing baselines . |
| Outcome: | The proposed model improves intent detection and entity extraction tasks without drop in recall. |
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks (2020.coling-industry)
Copied to clipboard
| Challenge: | Neural approaches have improved machine comprehension tasks, but models often operate as a black-box, resulting in lower interpretability. |
| Approach: | They propose a hybrid approach to quantify model uncertainty using Bayesian weight approximation and boost up inference speed by 80% relative to test time. |
| Outcome: | The proposed approach boosts inference speed by 80% relative to the previous approach and is applied to a clinical dialogue comprehension task. |
Regularized Graph Convolutional Networks for Short Text Classification (2020.coling-industry)
Copied to clipboard
| Challenge: | Short text classification is a problem in natural language processing, social network analysis, and e-commerce. |
| Approach: | They propose a short text classification technique that incorporates label dependencies into the output space to overcome the limitations of short text. |
| Outcome: | The proposed model outperforms baseline methods on proprietary and external datasets and is more robust to noise in textual features. |