pwc-archive/papers-with-abstracts huggingface dataset:
Discovering Better Model Architectures for Medical Query Understanding
Authors: Wei Zhu, Yuan Ni, Xiaoling Wang, Guotong Xie
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: model, Natural Language Inference, Neural Architecture Search, Question Answering, Sentence

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 our approach provides the best results.
Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States
Authors: Litton J Kurisinkel, Ai Ti Aw, Nancy F Chen
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Diversity, Informativeness, Sentence, Text Generation

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, informativeness and content diversity of generated radiology reports. For the purpose we propose a method to compute image representations specific to each sentential context and 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, relative on objective metrics and 16{\%} on human evaluation.
An Empirical Study of Generating Texts for Search Engine Advertising
Authors: Hidetaka Kamigaito, Peinan Zhang, Hiroya Takamura, Manabu Okumura
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Text Generation

Although there are many studies on neural language generation (NLG), few trials are put into the real world, especially in the advertising domain. Generating ads with NLG models can help copywriters in their creation. However, few studies have adequately evaluated the effect of generated ads with actual serving included because it requires a large amount of training data and a particular environment. In this paper, we demonstrate a practical use case of generating ad-text with an NLG model. Specially, we show how to improve the ads{'} impact, deploy models to a product, and evaluate the generated ads.
Ad Headline Generation using Self-Critical Masked Language Model
Authors: Yashal Shakti Kanungo, Sumit Negi, Aruna Rajan
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Headline Generation, Language Modeling, Language Modelling, Policy Gradient Methods, Reinforcement Learning (RL)

For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers. It is hard to pass the creative quality bar of the website, especially at a large scale. We thus propose a programmatic solution to generate product advertising headlines using retail content. We propose a state of the art application of Reinforcement Learning (RL) Policy gradient methods on Transformer (Vaswani et al., 2017) based Masked Language Models (Devlin et al., 2019). Our method creates the advertising headline by jointly conditioning on multiple products that a seller wishes to advertise. We demonstrate that our method outperforms existing Transformer and LSTM + RL methods in overlap metrics and quality audits. We also show that our model generated headlines outperform human submitted headlines in terms of both grammar and creative quality as determined by audits.
LATEX-Numeric: Language Agnostic Text Attribute Extraction for Numeric Attributes
Authors: Kartik Mehta, Ioana Oprea, Nikhil Rasiwasia
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Active Learning, Attribute, Attribute Extraction, Missing Labels, Multi-Task Learning

In this paper, we present LATEX-Numeric - a high-precision fully-automated scalable framework for extracting E-commerce numeric attributes from unstructured product text like product description. Most of the past work on attribute extraction is not scalable as they rely on manually curated training data, either with or without use of active learning. We rely on distant supervision for training data generation, removing dependency on manual labels. One issue with distant supervision is that it leads to incomplete training annotation due to missing attribute values while matching. We propose a multi-task learning architecture to deal with missing labels in the training data, leading to F1 improvement of 9.2{\%} for numeric attributes over state-of-the-art single-task architecture. While multi-task architecture benefits both numeric and non-numeric attributes, we present automated techniques to further improve the numeric attributes extraction models. Numeric attributes require a list of units (or aliases) for better matching with distant supervision. We propose an automated algorithm for alias creation using unstructured text and attribute values, leading to a 20.2{\%} F1 improvement. Extensive experiments on real world datasets for 20 numeric attributes across 5 product categories and 3 English marketplaces show that LATEX-numeric achieves a high F1-score, without any manual intervention, making it suitable for practical applications. Finally we show that the improvements are language-agnostic and LATEX-Numeric achieves 13.9{\%} F1 improvement for 3 non-English languages.
Training Language Models under Resource Constraints for Adversarial Advertisement Detection
Authors: Eshwar Shamanna Girishekar, Shiv Surya, Nishant Nikhil, Dyut Kumar Sil, Sumit Negi, Aruna Rajan
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Classification, Data Augmentation, text-classification, Text Classification

Advertising on e-commerce and social media sites deliver ad impressions at web scale on a daily basis driving value to both shoppers and advertisers. This scale necessitates programmatic ways of detecting unsuitable content in ads to safeguard customer experience and trust. This paper focusses on techniques for training text classification models under resource constraints, built as part of automated solutions for advertising content moderation. We show how weak supervision, curriculum learning and multi-lingual training can be applied effectively to fine-tune BERT and its variants for text classification tasks in conjunction with different data augmentation strategies. Our extensive experiments on multiple languages show that these techniques detect adversarial ad categories with a substantial gain in precision at high recall threshold over the baseline.
Combining Weakly Supervised ML Techniques for Low-Resource NLU
Authors: Victor Soto, Konstantine Arkoudas
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Continual Learning, Data Augmentation, Natural Language Understanding, Transfer Learning

Recent advances in transfer learning have improved the performance of virtual assistants considerably. Nevertheless, creating sophisticated voice-enabled applications for new domains remains a challenge, and meager training data is often a key bottleneck. Accordingly, unsupervised learning and SSL (semi-supervised learning) techniques continue to be of vital importance. While a number of such methods have been explored previously in isolation, in this paper we investigate the synergistic use of a number of weakly supervised techniques with a view to improving NLU (Natural Language Understanding) accuracy in low-resource settings. We explore three different approaches incorporating anonymized, unlabeled and automatically transcribed user utterances into the training process, two focused on data augmentation via SSL and another one focused on unsupervised and transfer learning. We show promising results, obtaining gains that range from 4.73{\%} to 7.65{\%} relative improvements on semantic error rate for each individual approach. Moreover, the combination of all three methods together yields a relative improvement of 11.77{\%} over our current baseline model. Our methods are applicable to any new domain with minimal training data, and can be deployed over time into a cycle of continual learning.
Label-Guided Learning for Item Categorization in e-Commerce
Authors: Lei Chen, Hirokazu Miyake
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Classification, text-classification, Text Classification

Item categorization is an important application of text classification in e-commerce due to its impact on the online shopping experience of users. One class of text classification techniques that has gained attention recently is using the semantic information of the labels to guide the classification task. We have conducted a systematic investigation of the potential benefits of these methods on a real data set from a major e-commerce company in Japan. Furthermore, using a hyperbolic space to embed product labels that are organized in a hierarchical structure led to better performance compared to using a conventional Euclidean space embedding. These findings demonstrate how label-guided learning can improve item categorization systems in the e-commerce domain.
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering
Authors: Minhao Zhang, Ruoyu Zhang, Lei Zou, Yinnian Lin, Sen Hu
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Data Augmentation, Knowledge Base Question Answering, Question Answering

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 techniques including data augmentation and multitasking, we show that the proposed framework outperforms the previous SoTA on 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, a video illustration (https://youtu.be/yetnVye{\_}hg4) of NAMER is also available.
FITAnnotator: A Flexible and Intelligent Text Annotation System
Authors: Yanzeng Li, Bowen Yu, Li Quangang, Tingwen Liu
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Active Learning, Incremental Learning, text annotation

In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation. Benefiting from the fully modular architecture design, FITAnnotator provides a systematic solution for the annotation of a variety of natural language processing tasks, including classification, sequence tagging and semantic role annotation, regardless of the language. Three kinds of interfaces are developed to annotate instances, evaluate annotation quality and manage the annotation task for annotators, reviewers and managers, respectively. FITAnnotator also gives intelligent annotations by introducing task-specific assistant to support and guide the annotators based on active learning and incremental learning strategies. This assistant is able to effectively update from the annotator feedbacks and easily handle the incremental labeling scenarios.
Multifaceted Domain-Specific Document Embeddings
Authors: Julian Risch, Philipp Hager, Ralf Krestel
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Document Embedding, Knowledge Graphs

Current document embeddings require large training corpora but fail to learn high-quality representations when confronted with a small number of domain-specific documents and rare terms. Further, they transform each document into a single embedding vector, making it hard to capture different notions of document similarity or explain why two documents are considered similar. In this work, we propose our Faceted Domain Encoder, a novel approach to learn multifaceted embeddings for domain-specific documents. It is based on a Siamese neural network architecture and leverages knowledge graphs to further enhance the embeddings even if only a few training samples are available. The model identifies different types of domain knowledge and encodes them into separate dimensions of the embedding, thereby enabling multiple ways of finding and comparing related documents in the vector space. We evaluate our approach on two benchmark datasets and find that it achieves the same embedding quality as state-of-the-art models while requiring only a tiny fraction of their training data. An interactive demo, our source code, and the evaluation datasets are available online: https://hpi.de/naumann/s/multifaceted-embeddings and a screencast is available on YouTube: https://youtu.be/HHcsX2clEwg
Improving Evidence Retrieval for Automated Explainable Fact-Checking
Authors: Chris Samarinas, Wynne Hsu, Mong Li Lee
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Articles, Fact Checking, Retrieval, Sentence

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. We describe a three-stage automated fact-checking system, named Quin+, using evidence retrieval and selection methods. We demonstrate that using dense passage representations leads to much higher evidence recall in a noisy setting. We also propose 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 results from web search engines.
Interactive Plot Manipulation using Natural Language
Authors: Yihan Wang, Yutong Shao, Ndapa Nakashole
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4

We present an interactive Plotting Agent, a system that enables users to directly manipulate plots using natural language instructions within an interactive programming environment. The Plotting Agent 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. This plotting model while accurate in most cases, still makes errors, therefore, the system allows a feedback mode, wherein the user is presented with a top-k list of plots, among which the user can pick the desired one. From this kind of feedback, we can then, in principle, continuously learn and improve the system. Given that plotting is widely used across data-driven fields, we believe our demonstration will be of interest to both practitioners such as data scientists broadly defined, and researchers interested in natural language interfaces.
ActiveAnno: General-Purpose Document-Level Annotation Tool with Active Learning Integration
Authors: Max Wiechmann, Seid Muhie Yimam, Chris Biemann
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: Active Learning

ActiveAnno is an annotation tool focused on document-level annotation tasks developed both for industry and research settings. It is designed to be a general-purpose tool with a wide variety of use cases. It features a modern and responsive web UI for creating annotation projects, conducting annotations, adjudicating disagreements, and analyzing annotation results. ActiveAnno embeds a highly configurable and interactive user interface. The tool also integrates a RESTful API that enables integration into other software systems, including an API for machine learning integration. ActiveAnno is built with extensible design and easy deployment in mind, all to enable users to perform annotation tasks with high efficiency and high-quality annotation results.
Supporting Spanish Writers using Automated Feedback
Authors: Aoife Cahill, James Bruno, James Ramey, Gilmar Ayala Meneses, Ian Blood, Florencia Tolentino, Tamar Lavee, Slava Andreyev
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4

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 third-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.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
Authors: Haoyang Wen, Ying Lin, Tuan Lai, Xiaoman Pan, Sha Li, Xudong Lin, Ben Zhou, Manling Li, Haoyu Wang, Hongming Zhang, Xiaodong Yu, Alexander Dong, Zhenhailong Wang, Yi Fung, Piyush Mishra, Qing Lyu, D{\'\i}dac Sur{\'\i}s, Brian Chen, Susan Windisch Brown, Martha Palmer, Chris Callison-Burch, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Heng Ji
Date: 2021-06-01 00:00:00
Proceeding: NAACL 2021 4
Tasks: coreference-resolution, Coreference Resolution, Event Extraction, Sentence

We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video). The system advances state-of-the-art from two aspects: (1) extending from sentence-level event extraction to cross-document cross-lingual cross-media event extraction, coreference resolution and temporal event tracking; (2) using human curated event schema library to match and enhance the extraction output. We have made the dockerlized system publicly available for research purpose at GitHub, with a demo video.
Unsupervised Natural Language Parsing (Introductory Tutorial)
Authors: Kewei Tu, Yong Jiang, Wenjuan Han, Yanpeng Zhao
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations. Recently, there has been a resurgence of interest in unsupervised parsing, which can be attributed to the combination of two trends in the NLP community: a general trend towards unsupervised training or pre-training, and an emerging trend towards finding or modeling linguistic structures in neural models. In this tutorial, we will introduce to the general audience what unsupervised parsing does and how it can be useful for and beyond syntactic parsing. We will then provide a systematic overview of major classes of approaches to unsupervised parsing, namely generative and discriminative approaches, and analyze their relative strengths and weaknesses. We will cover both decade-old statistical approaches and more recent neural approaches to give the audience a sense of the historical and recent development of the field. We will also discuss emerging research topics such as BERT-based approaches and visually grounded learning.
Aggregating and Learning from Multiple Annotators
Authors: Silviu Paun, Edwin Simpson
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

The success of NLP research is founded on high-quality annotated datasets, which are usually obtained from multiple expert annotators or crowd workers. The standard practice to training machine learning models is to first adjudicate the disagreements and then perform the training. To this end, there has been a lot of work on aggregating annotations, particularly for classification tasks. However, many other tasks, particularly in NLP, have unique characteristics not considered by standard models of annotation, e.g., label interdependencies in sequence labelling tasks, unrestricted labels for anaphoric annotation, or preference labels for ranking texts. In recent years, researchers have picked up on this and are covering the gap. A first objective of this tutorial is to connect NLP researchers with state-of-the-art aggregation models for a diverse set of canonical language annotation tasks. There is also a growing body of recent work arguing that following the convention and training with adjudicated labels ignores any uncertainty the labellers had in their classifications, which results in models with poorer generalisation capabilities. Therefore, a second objective of this tutorial is to teach NLP workers how they can augment their (deep) neural models to learn from data with multiple interpretations.
Tutorial Proposal: End-to-End Speech Translation
Authors: Jan Niehues, Elizabeth Salesky, Marco Turchi, Matteo Negri
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Translation

Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. Speech translation has attracted interest for many years, but the recent successful applications of deep learning to both individual tasks have enabled new opportunities through joint modeling, in what we today call {`}end-to-end speech translation.{'} In this tutorial we will introduce the techniques used in cutting-edge research on speech translation. Starting from the traditional cascaded approach, we will given an overview on data sources and model architectures to achieve state-of-the art performance with end-to-end speech translation for both high- and low-resource languages. In addition, we will discuss methods to evaluate analyze the proposed solutions, as well as the challenges faced when applying speech translation models for real-world applications.
Advances and Challenges in Unsupervised Neural Machine Translation
Authors: Rui Wang, Hai Zhao
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2
Tasks: Denoising, Machine Translation, Translation

Unsupervised cross-lingual language representation initialization methods, together with mechanisms such as denoising and back-translation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results. Meanwhile, there are still several challenges for UNMT. This tutorial first introduces the background and the latest progress of UNMT. We then examine a number of challenges to UNMT and give empirical results on how well the technology currently holds up.
Do we read what we hear? Modeling orthographic influences on spoken word recognition
Authors: Nicole Macher, Badr M. Abdullah, Harm Brouwer, Dietrich Klakow
Date: 2021-04-01 00:00:00
Proceeding: EACL 2021 2

Theories and models of spoken word recognition aim to explain the process of accessing lexical knowledge given an acoustic realization of a word form. There is consensus that phonological and semantic information is crucial for this process. However, there is accumulating evidence that orthographic information could also have an impact on auditory word recognition. This paper presents two models of spoken word recognition that instantiate different hypotheses regarding the influence of orthography on this process. We show that these models reproduce human-like behavior in different ways and provide testable hypotheses for future research on the source of orthographic effects in spoken word recognition.