pwc-archive/papers-with-abstracts huggingface dataset:
Joint Semantics and Data-Driven Path Representation for Knowledge Graph Inference
Authors: Guanglin Niu, Bo Li, Yongfei Zhang, Yongpan Sheng, Chuan Shi, Jingyang Li, ShiLiang Pu
Date: 2020-10-06 00:00:00
Tasks: Link Prediction, Question Answering

Inference on a large-scale knowledge graph (KG) is of great importance for KG applications like question answering. The path-based reasoning models can leverage much information over paths other than pure triples in the KG, which face several challenges: all the existing path-based methods are data-driven, lacking explainability for path representation. Besides, some methods either consider only relational paths or ignore the heterogeneity between entities and relations both contained in paths, which cannot capture the rich semantics of paths well. To address the above challenges, in this work, we propose a novel joint semantics and data-driven path representation that balances explainability and generalization in the framework of KG embedding. More specifically, we inject horn rules to obtain the condensed paths by the transparent and explainable path composition procedure. The entity converter is designed to transform the entities along paths into the representations in the semantic level similar to relations for reducing the heterogeneity between entities and relations, in which the KGs both with and without type information are considered. Our proposed model is evaluated on two classes of tasks: link prediction and path query answering task. The experimental results show that it has a significant performance gain over several different state-of-the-art baselines.
Converting the Point of View of Messages Spoken to Virtual Assistants
Authors: Isabelle G. Lee, Vera Zu, Sai Srujana Buddi, Dennis Liang, Jack G. M. FitzGerald
Date: 2020-10-06 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Constituency Parsing, Language Modelling, Machine Translation, NMT, Part-Of-Speech Tagging, text-classification, Text Classification, Translation

Virtual Assistants can be quite literal at times. If the user says "tell Bob I love him," most virtual assistants will extract the message "I love him" and send it to the user's contact named Bob, rather than properly converting the message to "I love you." We designed a system to allow virtual assistants to take a voice message from one user, convert the point of view of the message, and then deliver the result to its target user. We developed a rule-based model, which integrates a linear text classification model, part-of-speech tagging, and constituency parsing with rule-based transformation methods. We also investigated Neural Machine Translation (NMT) approaches, including LSTMs, CopyNet, and T5. We explored 5 metrics to gauge both naturalness and faithfulness automatically, and we chose to use BLEU plus METEOR for faithfulness and relative perplexity using a separately trained language model (GPT) for naturalness. Transformer-Copynet and T5 performed similarly on faithfulness metrics, with T5 achieving slight edge, a BLEU score of 63.8 and a METEOR score of 83.0. CopyNet was the most natural, with a relative perplexity of 1.59. CopyNet also has 37 times fewer parameters than T5. We have publicly released our dataset, which is composed of 46,565 crowd-sourced samples.
Embedding Words in Non-Vector Space with Unsupervised Graph Learning
Authors: Max Ryabinin, Sergei Popov, Liudmila Prokhorenkova, Elena Voita
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Graph Learning, Word Embeddings, Word Similarity

It has become a de-facto standard to represent words as elements of a vector space (word2vec, GloVe). While this approach is convenient, it is unnatural for language: words form a graph with a latent hierarchical structure, and this structure has to be revealed and encoded by word embeddings. We introduce GraphGlove: unsupervised graph word representations which are learned end-to-end. In our setting, each word is a node in a weighted graph and the distance between words is the shortest path distance between the corresponding nodes. We adopt a recent method learning a representation of data in the form of a differentiable weighted graph and use it to modify the GloVe training algorithm. We show that our graph-based representations substantially outperform vector-based methods on word similarity and analogy tasks. Our analysis reveals that the structure of the learned graphs is hierarchical and similar to that of WordNet, the geometry is highly non-trivial and contains subgraphs with different local topology.
Semantically Driven Sentence Fusion: Modeling and Evaluation
Authors: Eyal Ben-David, Orgad Keller, Eric Malmi, Idan Szpektor, Roi Reichart
Date: 2020-10-06 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Sentence, Sentence Fusion, valid

Sentence fusion is the task of joining related sentences into coherent text. Current training and evaluation schemes for this task are based on single reference ground-truths and do not account for valid fusion variants. We show that this hinders models from robustly capturing the semantic relationship between input sentences. To alleviate this, we present an approach in which ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. We apply this method to a large-scale dataset and use the augmented dataset for both model training and evaluation. To improve the learning of semantic representation using multiple references, we enrich the model with auxiliary discourse classification tasks under a multi-tasking framework. Our experiments highlight the improvements of our approach over state-of-the-art models.
Scene Graph Modification Based on Natural Language Commands
Authors: Xuanli He, Quan Hung Tran, Gholamreza Haffari, Walter Chang, Trung Bui, Zhe Lin, Franck Dernoncourt, Nhan Dam
Date: 2020-10-06 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Graph Generation, Machine Translation, Translation

Structured representations like graphs and parse trees play a crucial role in many Natural Language Processing systems. In recent years, the advancements in multi-turn user interfaces necessitate the need for controlling and updating these structured representations given new sources of information. Although there have been many efforts focusing on improving the performance of the parsers that map text to graphs or parse trees, very few have explored the problem of directly manipulating these representations. In this paper, we explore the novel problem of graph modification, where the systems need to learn how to update an existing scene graph given a new user's command. Our novel models based on graph-based sparse transformer and cross attention information fusion outperform previous systems adapted from the machine translation and graph generation literature. We further contribute our large graph modification datasets to the research community to encourage future research for this new problem.
CoRefi: A Crowd Sourcing Suite for Coreference Annotation
Authors: Aaron Bornstein, Arie Cattan, Ido Dagan
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Coreference Resolution, Cross Document Coreference Resolution

Coreference annotation is an important, yet expensive and time consuming, task, which often involved expert annotators trained on complex decision guidelines. To enable cheaper and more efficient annotation, we present CoRefi, a web-based coreference annotation suite, oriented for crowdsourcing. Beyond the core coreference annotation tool, CoRefi provides guided onboarding for the task as well as a novel algorithm for a reviewing phase. CoRefi is open source and directly embeds into any website, including popular crowdsourcing platforms. CoRefi Demo: aka.ms/corefi Video Tour: aka.ms/corefivideo Github Repo: https://github.com/aribornstein/corefi
Dissecting Span Identification Tasks with Performance Prediction
Authors: Sean Papay, Roman Klinger, Sebastian Padó
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Chunking, NER, Prediction

Span identification (in short, span ID) tasks such as chunking, NER, or code-switching detection, ask models to identify and classify relevant spans in a text. Despite being a staple of NLP, and sharing a common structure, there is little insight on how these tasks' properties influence their difficulty, and thus little guidance on what model families work well on span ID tasks, and why. We analyze span ID tasks via performance prediction, estimating how well neural architectures do on different tasks. Our contributions are: (a) we identify key properties of span ID tasks that can inform performance prediction; (b) we carry out a large-scale experiment on English data, building a model to predict performance for unseen span ID tasks that can support architecture choices; (c), we investigate the parameters of the meta model, yielding new insights on how model and task properties interact to affect span ID performance. We find, e.g., that span frequency is especially important for LSTMs, and that CRFs help when spans are infrequent and boundaries non-distinctive.
Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles
Authors: Carel van Niekerk, Michael Heck, Christian Geishauser, Hsien-Chin Lin, Nurul Lubis, Marco Moresi, Milica Gašić
Date: 2020-10-06 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020

The ability to accurately track what happens during a conversation is essential for the performance of a dialogue system. Current state-of-the-art multi-domain dialogue state trackers achieve just over 55% accuracy on the current go-to benchmark, which means that in almost every second dialogue turn they place full confidence in an incorrect dialogue state. Belief trackers, on the other hand, maintain a distribution over possible dialogue states. However, they lack in performance compared to dialogue state trackers, and do not produce well calibrated distributions. In this work we present state-of-the-art performance in calibration for multi-domain dialogue belief trackers using a calibrated ensemble of models. Our resulting dialogue belief tracker also outperforms previous dialogue belief tracking models in terms of accuracy.
Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start
Authors: Wenpeng Yin, Nazneen Fatema Rajani, Dragomir Radev, Richard Socher, Caiming Xiong
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: coreference-resolution, Coreference Resolution, Natural Language Inference, Question Answering

A standard way to address different NLP problems is by first constructing a problem-specific dataset, then building a model to fit this dataset. To build the ultimate artificial intelligence, we desire a single machine that can handle diverse new problems, for which task-specific annotations are limited. We bring up textual entailment as a unified solver for such NLP problems. However, current research of textual entailment has not spilled much ink on the following questions: (i) How well does a pretrained textual entailment system generalize across domains with only a handful of domain-specific examples? and (ii) When is it worth transforming an NLP task into textual entailment? We argue that the transforming is unnecessary if we can obtain rich annotations for this task. Textual entailment really matters particularly when the target NLP task has insufficient annotations. Universal NLP can be probably achieved through different routines. In this work, we introduce Universal Few-shot textual Entailment (UFO-Entail). We demonstrate that this framework enables a pretrained entailment model to work well on new entailment domains in a few-shot setting, and show its effectiveness as a unified solver for several downstream NLP tasks such as question answering and coreference resolution when the end-task annotations are limited. Code: https://github.com/salesforce/UniversalFewShotNLP
Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients
Authors: Zijian Lei, Liang Lan
Date: 2020-10-06 00:00:00
Tasks: Binary Classification

Kernel approximation is widely used to scale up kernel SVM training and prediction. However, the memory and computation costs of kernel approximation models are still too high if we want to deploy them on memory-limited devices such as mobile phones, smartwatches, and IoT devices. To address this challenge, we propose a novel memory and computation-efficient kernel SVM model by using both binary embedding and binary model coefficients. First, we propose an efficient way to generate compact binary embedding of the data, preserving the kernel similarity. Second, we propose a simple but effective algorithm to learn a linear classification model with ternary coefficients that can support different types of loss function and regularizer. Our algorithm can achieve better generalization accuracy than existing works on learning binary coefficients since we allow coefficient to be $-1$, $0$, or $1$ during the training stage, and coefficient $0$ can be removed during model inference for binary classification. Moreover, we provide a detailed analysis of the convergence of our algorithm and the inference complexity of our model. The analysis shows that the convergence to a local optimum is guaranteed, and the inference complexity of our model is much lower than other competing methods. Our experimental results on five large real-world datasets have demonstrated that our proposed method can build accurate nonlinear SVM models with memory costs less than 30KB.
A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning
Authors: Alexander Mey
Date: 2020-10-06 00:00:00
Tasks: BIG-bench Machine Learning, Generalization Bounds, Learning Theory

Statistical machine learning theory often tries to give generalization guarantees of machine learning models. Those models naturally underlie some fluctuation, as they are based on a data sample. If we were unlucky, and gathered a sample that is not representative of the underlying distribution, one cannot expect to construct a reliable machine learning model. Following that, statements made about the performance of machine learning models have to take the sampling process into account. The two common approaches for that are to generate statements that hold either in high-probability, or in-expectation, over the random sampling process. In this short note we show how one may transform one statement to another. As a technical novelty we address the case of unbounded loss function, where we use a fairly new assumption, called the witness condition.
Gaussian Process Models with Low-Rank Correlation Matrices for Both Continuous and Categorical Inputs
Authors: Dominik Kirchhoff, Sonja Kuhnt
Date: 2020-10-06 00:00:00

We introduce a method that uses low-rank approximations of cross-correlation matrices in mixed continuous and categorical Gaussian Process models. This new method -- called Low-Rank Correlation (LRC) -- offers the ability to flexibly adapt the number of parameters to the problem at hand by choosing an appropriate rank of the approximation. Furthermore, we present a systematic approach of defining test functions that can be used for assessing the accuracy of models or optimization methods that are concerned with both continuous and categorical inputs. We compare LRC to existing approaches of modeling the cross-correlation matrix. It turns out that the new approach performs well in terms of estimation of cross-correlations and response surface prediction. Therefore, LRC is a flexible and useful addition to existing methods, especially for increasing numbers of combinations of levels of the categorical inputs.
The Multilingual Amazon Reviews Corpus
Authors: Phillip Keung, Yichao Lu, György Szarvas, Noah A. Smith
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Classification, Cross-Lingual Transfer, General Classification, Multilingual text classification, text-classification, Text Classification, Transfer Learning, Zero-Shot Cross-Lingual Transfer

We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, Japanese, German, French, Spanish, and Chinese, which were collected between 2015 and 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID, and the coarse-grained product category (e.g., 'books', 'appliances', etc.) The corpus is balanced across the 5 possible star ratings, so each rating constitutes 20% of the reviews in each language. For each language, there are 200,000, 5,000, and 5,000 reviews in the training, development, and test sets, respectively. We report baseline results for supervised text classification and zero-shot cross-lingual transfer learning by fine-tuning a multilingual BERT model on reviews data. We propose the use of mean absolute error (MAE) instead of classification accuracy for this task, since MAE accounts for the ordinal nature of the ratings.
Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution
Authors: Yordan Yordanov, Oana-Maria Camburu, Vid Kocijan, Thomas Lukasiewicz
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Semantic Similarity, Semantic Textual Similarity

Hard cases of pronoun resolution have been used as a long-standing benchmark for commonsense reasoning. In the recent literature, pre-trained language models have been used to obtain state-of-the-art results on pronoun resolution. Overall, four categories of training and evaluation objectives have been introduced. The variety of training datasets and pre-trained language models used in these works makes it unclear whether the choice of training objective is critical. In this work, we make a fair comparison of the performance and seed-wise stability of four models that represent the four categories of objectives. Our experiments show that the objective of sequence ranking performs the best in-domain, while the objective of semantic similarity between candidates and pronoun performs the best out-of-domain. We also observe a seed-wise instability of the model using sequence ranking, which is not the case when the other objectives are used.
StyleDGPT: Stylized Response Generation with Pre-trained Language Models
Authors: Ze Yang, Wei Wu, Can Xu, Xinnian Liang, Jiaqi Bai, Liran Wang, Wei Wang, Zhoujun Li
Date: 2020-10-06 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Response Generation, Sentence

Generating responses following a desired style has great potentials to extend applications of open-domain dialogue systems, yet is refrained by lacking of parallel data for training. In this work, we explore the challenging task with pre-trained language models that have brought breakthrough to various natural language tasks. To this end, we introduce a KL loss and a style classifier to the fine-tuning step in order to steer response generation towards the target style in both a word-level and a sentence-level. Comprehensive empirical studies with two public datasets indicate that our model can significantly outperform state-of-the-art methods in terms of both style consistency and contextual coherence.
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy
Authors: Umanga Bista, Alexander Patrick Mathews, Aditya Krishna Menon, Lexing Xie
Date: 2020-10-06 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Diversity, Document Summarization, Extractive Summarization, Multi-Document Summarization, Sentence, Two-sample testing

Most work on multi-document summarization has focused on generic summarization of information present in each individual document set. However, the under-explored setting of update summarization, where the goal is to identify the new information present in each set, is of equal practical interest (e.g., presenting readers with updates on an evolving news topic). In this work, we present SupMMD, a novel technique for generic and update summarization based on the maximum mean discrepancy from kernel two-sample testing. SupMMD combines both supervised learning for salience and unsupervised learning for coverage and diversity. Further, we adapt multiple kernel learning to make use of similarity across multiple information sources (e.g., text features and knowledge based concepts). We show the efficacy of SupMMD in both generic and update summarization tasks by meeting or exceeding the current state-of-the-art on the DUC-2004 and TAC-2009 datasets.
Disentangle-based Continual Graph Representation Learning
Authors: Xiaoyu Kou, Yankai Lin, Shaobo Liu, Peng Li, Jie zhou, Yan Zhang
Date: 2020-10-06 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Continual Learning, Graph Embedding, Graph Representation Learning, Representation Learning

Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since it overlooked the streaming nature of incoming data. To address this issue, we study the problem of continual graph representation learning which aims to continually train a GE model on new data to learn incessantly emerging multi-relational data while avoiding catastrophically forgetting old learned knowledge. Moreover, we propose a disentangle-based continual graph representation learning (DiCGRL) framework inspired by the human's ability to learn procedural knowledge. The experimental results show that DiCGRL could effectively alleviate the catastrophic forgetting problem and outperform state-of-the-art continual learning models.
Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher
Authors: Giannis Karamanolakis, Daniel Hsu, Luis Gravano
Date: 2020-10-06 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: General Classification, Representation Learning, text-classification, Text Classification

Cross-lingual text classification alleviates the need for manually labeled documents in a target language by leveraging labeled documents from other languages. Existing approaches for transferring supervision across languages require expensive cross-lingual resources, such as parallel corpora, while less expensive cross-lingual representation learning approaches train classifiers without target labeled documents. In this work, we propose a cross-lingual teacher-student method, CLTS, that generates "weak" supervision in the target language using minimal cross-lingual resources, in the form of a small number of word translations. Given a limited translation budget, CLTS extracts and transfers only the most important task-specific seed words across languages and initializes a teacher classifier based on the translated seed words. Then, CLTS iteratively trains a more powerful student that also exploits the context of the seed words in unlabeled target documents and outperforms the teacher. CLTS is simple and surprisingly effective in 18 diverse languages: by transferring just 20 seed words, even a bag-of-words logistic regression student outperforms state-of-the-art cross-lingual methods (e.g., based on multilingual BERT). Moreover, CLTS can accommodate any type of student classifier: leveraging a monolingual BERT student leads to further improvements and outperforms even more expensive approaches by up to 12% in accuracy. Finally, CLTS addresses emerging tasks in low-resource languages using just a small number of word translations.