Ernie Chang


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Logic-Guided Message Generation from Raw Real-Time Sensor Data
Ernie Chang | Alisa Kovtunova | Stefan Borgwardt | Vera Demberg | Kathryn Chapman | Hui-Syuan Yeh
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Natural language generation in real-time settings with raw sensor data is a challenging task. We find that formulating the task as an end-to-end problem leads to two major challenges in content selection – the sensor data is both redundant and diverse across environments, thereby making it hard for the encoders to select and reason on the data. We here present a new corpus for a specific domain that instantiates these properties. It includes handover utterances that an assistant for a semi-autonomous drone uses to communicate with humans during the drone flight. The corpus consists of sensor data records and utterances in 8 different environments. As a structured intermediary representation between data records and text, we explore the use of description logic (DL). We also propose a neural generation model that can alert the human pilot of the system state and environment in preparation of the handover of control.

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Programmable Annotation with Diversed Heuristics and Data Denoising
Ernie Chang | Alex Marin | Vera Demberg
Proceedings of the 29th International Conference on Computational Linguistics

Neural natural language generation (NLG) and understanding (NLU) models are costly and require massive amounts of annotated data to be competitive. Recent data programming frameworks address this bottleneck by allowing human supervision to be provided as a set of labeling functions to construct generative models that synthesize weak labels at scale. However, these labeling functions are difficult to build from scratch for NLG/NLU models, as they often require complex rule sets to be specified. To this end, we propose a novel data programming framework that can jointly construct labeled data for language generation and understanding tasks – by allowing the annotators to modify an automatically-inferred alignment rule set between sequence labels and text, instead of writing rules from scratch. Further, to mitigate the effect of poor quality labels, we propose a dually-regularized denoising mechanism for optimizing the NLU and NLG models. On two benchmarks we show that the framework can generate high-quality data that comes within a 1.48 BLEU and 6.42 slot F1 of the 100% human-labeled data (42k instances) with just 100 labeled data samples – outperforming benchmark annotation frameworks and other semi-supervised approaches.

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Few-Shot Pidgin Text Adaptation via Contrastive Fine-Tuning
Ernie Chang | Jesujoba O. Alabi | David Ifeoluwa Adelani | Vera Demberg
Proceedings of the 29th International Conference on Computational Linguistics

The surging demand for multilingual dialogue systems often requires a costly labeling process for each language addition. For low resource languages, human annotators are continuously tasked with the adaptation of resource-rich language utterances for each new domain. However, this prohibitive and impractical process can often be a bottleneck for low resource languages that are still without proper translation systems nor parallel corpus. In particular, it is difficult to obtain task-specific low resource language annotations for the English-derived creoles (e.g. Nigerian and Cameroonian Pidgin). To address this issue, we utilize the pretrained language models i.e. BART which has shown great potential in language generation/understanding – we propose to finetune the BART model to generate utterances in Pidgin by leveraging the proximity of the source and target languages, and utilizing positive and negative examples in constrastive training objectives. We collected and released the first parallel Pidgin-English conversation corpus in two dialogue domains and showed that this simple and effective technique is suffice to yield impressive results for English-to-Pidgin generation, which are two closely-related languages.

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Improving Zero-Shot Multilingual Text Generation via Iterative Distillation
Ernie Chang | Alex Marin | Vera Demberg
Proceedings of the 29th International Conference on Computational Linguistics

The demand for multilingual dialogue systems often requires a costly labeling process, where human translators derive utterances in low resource languages from resource rich language annotation. To this end, we explore leveraging the inductive biases for target languages learned by numerous pretrained teacher models by transferring them to student models via sequence-level knowledge distillation. By assuming no target language text, the both the teacher and student models need to learn from the target distribution in a few/zero-shot manner. On the MultiATIS++ benchmark, we explore the effectiveness of our proposed technique to derive the multilingual text for 6 languages, using only the monolingual English data and the pretrained models. We show that training on the synthetic multilingual generation outputs yields close performance to training on human annotations in both slot F1 and intent accuracy; the synthetic text also scores high in naturalness and correctness based on human evaluation.

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Two-Stage Movie Script Summarization: An Efficient Method For Low-Resource Long Document Summarization
Dongqi Pu | Xudong Hong | Pin-Jie Lin | Ernie Chang | Vera Demberg
Proceedings of The Workshop on Automatic Summarization for Creative Writing

The Creative Summarization Shared Task at COLING 2022 aspires to generate summaries given long-form texts from creative writing. This paper presents the system architecture and the results of our participation in the Scriptbase track that focuses on generating movie plots given movie scripts. The core innovation in our model employs a two-stage hierarchical architecture for movie script summarization. In the first stage, a heuristic extraction method is applied to extract actions and essential dialogues, which reduces the average length of input movie scripts by 66% from about 24K to 8K tokens. In the second stage, a state-of-the-art encoder-decoder model, Longformer-Encoder-Decoder (LED), is trained with effective fine-tuning methods, BitFit and NoisyTune. Evaluations on the unseen test set indicate that our system outperforms both zero-shot LED baselines as well as other participants on various automatic metrics and ranks 1st in the Scriptbase track.

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A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
David Adelani | Jesujoba Alabi | Angela Fan | Julia Kreutzer | Xiaoyu Shen | Machel Reid | Dana Ruiter | Dietrich Klakow | Peter Nabende | Ernie Chang | Tajuddeen Gwadabe | Freshia Sackey | Bonaventure F. P. Dossou | Chris Emezue | Colin Leong | Michael Beukman | Shamsuddeen Muhammad | Guyo Jarso | Oreen Yousuf | Andre Niyongabo Rubungo | Gilles Hacheme | Eric Peter Wairagala | Muhammad Umair Nasir | Benjamin Ajibade | Tunde Ajayi | Yvonne Gitau | Jade Abbott | Mohamed Ahmed | Millicent Ochieng | Anuoluwapo Aremu | Perez Ogayo | Jonathan Mukiibi | Fatoumata Ouoba Kabore | Godson Kalipe | Derguene Mbaye | Allahsera Auguste Tapo | Victoire Memdjokam Koagne | Edwin Munkoh-Buabeng | Valencia Wagner | Idris Abdulmumin | Ayodele Awokoya | Happy Buzaaba | Blessing Sibanda | Andiswa Bukula | Sam Manthalu
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.


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The SelectGen Challenge: Finding the Best Training Samples for Few-Shot Neural Text Generation
Ernie Chang | Xiaoyu Shen | Alex Marin | Vera Demberg
Proceedings of the 14th International Conference on Natural Language Generation

We propose a shared task on training instance selection for few-shot neural text generation. Large-scale pretrained language models have led to dramatic improvements in few-shot text generation. Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. Studying the selection strategy can help us (1) make the most use of our annotation budget in downstream tasks and (2) better benchmark few-shot text generative models. We welcome submissions that present their selection strategies and the effects on the generation quality.

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Time-Aware Ancient Chinese Text Translation and Inference
Ernie Chang | Yow-Ting Shiue | Hui-Syuan Yeh | Vera Demberg
Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021

In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations that are poor in quality, and (2) most translations are missing the contextual information that is often very crucial to understanding the text. To this end, we improve upon past translation techniques by proposing the following: We reframe the task as a multi-label prediction task where the model predicts both the translation and its particular era. We observe that this helps to bridge the linguistic gap as chronological context is also used as auxiliary information. We validate our framework on a parallel corpus annotated with chronology information and show experimentally its efficacy in producing quality translation outputs. We release both the code and the data for future research.

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On Training Instance Selection for Few-Shot Neural Text Generation
Ernie Chang | Xiaoyu Shen | Hui-Syuan Yeh | Vera Demberg
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. In this work, we present a study on training instance selection in few-shot neural text generation. The selection decision is made based only on the unlabeled data so as to identify the most worthwhile data points that should be annotated under some budget of labeling cost. Based on the intuition that the few-shot training instances should be diverse and representative of the entire data distribution, we propose a simple selection strategy with K-means clustering. We show that even with the naive clustering-based approach, the generation models consistently outperform random sampling on three text generation tasks: data-to-text generation, document summarization and question generation. The code and training data are made available. We hope that this work will call for more attention on this largely unexplored area.

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Does the Order of Training Samples Matter? Improving Neural Data-to-Text Generation with Curriculum Learning
Ernie Chang | Hui-Syuan Yeh | Vera Demberg
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Recent advancements in data-to-text generation largely take on the form of neural end-to-end systems. Efforts have been dedicated to improving text generation systems by changing the order of training samples in a process known as curriculum learning. Past research on sequence-to-sequence learning showed that curriculum learning helps to improve both the performance and convergence speed. In this work, we delve into the same idea surrounding the training samples consisting of structured data and text pairs, where at each update, the curriculum framework selects training samples based on the model’s competence. Specifically, we experiment with various difficulty metrics and put forward a soft edit distance metric for ranking training samples. On our benchmarks, it shows faster convergence speed where training time is reduced by 38.7% and performance is boosted by 4.84 BLEU.

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Neural Data-to-Text Generation with LM-based Text Augmentation
Ernie Chang | Xiaoyu Shen | Dawei Zhu | Vera Demberg | Hui Su
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

For many new application domains for data-to-text generation, the main obstacle in training neural models consists of a lack of training data. While usually large numbers of instances are available on the data side, often only very few text samples are available. To address this problem, we here propose a novel few-shot approach for this setting. Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples. As the text augmentation can introduce noise to the training data, we use cycle consistency as an objective, in order to make sure that a given data sample can be correctly reconstructed after having been formulated as text (and that text samples can be reconstructed from data). On both the E2E and WebNLG benchmarks, we show that this weakly supervised training paradigm is able to outperform fully supervised sequence-to-sequence models with less than 10% of the training set. By utilizing all annotated data, our model can boost the performance of a standard sequence-to-sequence model by over 5 BLEU points, establishing a new state-of-the-art on both datasets.

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Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling
Ernie Chang | Vera Demberg | Alex Marin
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address this bottleneck with generative models that synthesize weak labels at scale, where a small amount of training labels are expert-curated and the rest of the data is automatically annotated. We follow that approach, by automatically constructing a large-scale weakly-labeled data with a fine-tuned GPT-2, and employ a semi-supervised framework to jointly train the NLG and NLU models. The proposed framework adapts the parameter updates to the models according to the estimated label-quality. On both the E2E and Weather benchmarks, we show that this weakly supervised training paradigm is an effective approach under low resource scenarios with as little as 10 data instances, and outperforming benchmark systems on both datasets when 100% of the training data is used.


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Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence
Xiaoyu Shen | Ernie Chang | Hui Su | Cheng Niu | Dietrich Klakow
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

The neural attention model has achieved great success in data-to-text generation tasks. Though usually excelling at producing fluent text, it suffers from the problem of information missing, repetition and “hallucination”. Due to the black-box nature of the neural attention architecture, avoiding these problems in a systematic way is non-trivial. To address this concern, we propose to explicitly segment target text into fragment units and align them with their data correspondences. The segmentation and correspondence are jointly learned as latent variables without any human annotations. We further impose a soft statistical constraint to regularize the segmental granularity. The resulting architecture maintains the same expressive power as neural attention models, while being able to generate fully interpretable outputs with several times less computational cost. On both E2E and WebNLG benchmarks, we show the proposed model consistently outperforms its neural attention counterparts.

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DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool
Ernie Chang | Jeriah Caplinger | Alex Marin | Xiaoyu Shen | Vera Demberg
Proceedings of the 28th International Conference on Computational Linguistics: System Demonstrations

We present a lightweight annotation tool, the Data AnnotatoR Tool (DART), for the general task of labeling structured data with textual descriptions. The tool is implemented as an interactive application that reduces human efforts in annotating large quantities of structured data, e.g. in the format of a table or tree structure. By using a backend sequence-to-sequence model, our system iteratively analyzes the annotated labels in order to better sample unlabeled data. In a simulation experiment performed on annotating large quantities of structured data, DART has been shown to reduce the total number of annotations needed with active learning and automatically suggesting relevant labels.

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MovieChats: Chat like Humans in a Closed Domain
Hui Su | Xiaoyu Shen | Zhou Xiao | Zheng Zhang | Ernie Chang | Cheng Zhang | Cheng Niu | Jie Zhou
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Being able to perform in-depth chat with humans in a closed domain is a precondition before an open-domain chatbot can be ever claimed. In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots. We propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show this simple neural approach trained on high-quality data is able to outperform commercial systems replying on complex rules. On both the static and interactive tests, we find responses generated by our system exhibits remarkably good engagement and sensibleness close to human-written ones. We further analyze the limits of our work and point out potential directions for future work


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Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders
Xudong Hong | Ernie Chang | Vera Demberg
Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)

The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features. This paper describes the results of our participation in the deep track. The core innovation in our approach is to use a graph convolutional network to encode the dependency trees given as input. Upon adding morphological features, our system achieves the third rank without using data augmentation techniques or additional components (such as a re-ranker).


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Generating E-Commerce Product Titles and Predicting their Quality
José G. Camargo de Souza | Michael Kozielski | Prashant Mathur | Ernie Chang | Marco Guerini | Matteo Negri | Marco Turchi | Evgeny Matusov
Proceedings of the 11th International Conference on Natural Language Generation

E-commerce platforms present products using titles that summarize product information. These titles cannot be created by hand, therefore an algorithmic solution is required. The task of automatically generating these titles given noisy user provided titles is one way to achieve the goal. The setting requires the generation process to be fast and the generated title to be both human-readable and concise. Furthermore, we need to understand if such generated titles are usable. As such, we propose approaches that (i) automatically generate product titles, (ii) predict their quality. Our approach scales to millions of products and both automatic and human evaluations performed on real-world data indicate our approaches are effective and applicable to existing e-commerce scenarios.


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Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model
WenLi Zhuang | Ernie Chang
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Arabic.