pwc-archive/papers-with-abstracts huggingface dataset:
What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues
Authors: Yu Qin, Yi Yang
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

Predicting financial risk is an essential task in financial market. Prior research has shown that textual information in a firm{'}s financial statement can be used to predict its stock{'}s risk level. Nowadays, firm CEOs communicate information not only verbally through press releases and financial reports, but also nonverbally through investor meetings and earnings conference calls. There are anecdotal evidences that CEO{'}s vocal features, such as emotions and voice tones, can reveal the firm{'}s performance. However, how vocal features can be used to predict risk levels, and to what extent, is still unknown. To fill the gap, we obtain earnings call audio recordings and textual transcripts for S{\&}P 500 companies in recent years. We propose a multimodal deep regression model (MDRM) that jointly model CEO{'}s verbal (from text) and vocal (from audio) information in a conference call. Empirical results show that our model that jointly considers verbal and vocal features achieves significant and substantial prediction error reduction. We also discuss several interesting findings and the implications to financial markets. The processed earnings conference calls data (text and audio) are released for readers who are interested in reproducing the results or designing trading strategy.
Detecting Concealed Information in Text and Speech
Authors: Shengli Hu
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Deception Detection, Multi-Task Learning

Motivated by infamous cheating scandals in the media industry, the wine industry, and political campaigns, we address the problem of detecting concealed information in technical settings. In this work, we explore acoustic-prosodic and linguistic indicators of information concealment by collecting a unique corpus of professionals practicing for oral exams while concealing information. We reveal subtle signs of concealing information in speech and text, compare and contrast them with those in deception detection literature, uncovering the link between concealing information and deception. We then present a series of experiments that automatically detect concealed information from text and speech. We compare the use of acoustic-prosodic, linguistic, and individual feature sets, using different machine learning models. Finally, we present a multi-task learning framework with acoustic, linguistic, and individual features, that outperforms human performance by over 15{\%}.
Evidence-based Trustworthiness
Authors: Yi Zhang, Zachary Ives, Dan Roth
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Information Retrieval, Retrieval

The information revolution brought with it information pollution. Information retrieval and extraction help us cope with abundant information from diverse sources. But some sources are of anonymous authorship, and some are of uncertain accuracy, so how can we determine what we should actually believe? Not all information sources are equally trustworthy, and simply accepting the majority view is often wrong. This paper develops a general framework for estimating the trustworthiness of information sources in an environment where multiple sources provide claims and supporting evidence, and each claim can potentially be produced by multiple sources. We consider two settings: one in which information sources directly assert claims, and a more realistic and challenging one, in which claims are inferred from evidence provided by sources, via (possibly noisy) NLP techniques. Our key contribution is to develop a family of probabilistic models that jointly estimate the trustworthiness of sources, and the credibility of claims they assert. This is done while accounting for the (possibly noisy) NLP needed to infer claims from evidence supplied by sources. We evaluate our framework on several datasets, showing strong results and significant improvement over baselines.
Cross-Sentence Grammatical Error Correction
Authors: Shamil Chollampatt, Weiqi Wang, Hwee Tou Ng
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder, Grammatical Error Correction, Sentence

Automatic grammatical error correction (GEC) research has made remarkable progress in the past decade. However, all existing approaches to GEC correct errors by considering a single sentence alone and ignoring crucial cross-sentence context. Some errors can only be corrected reliably using cross-sentence context and models can also benefit from the additional contextual information in correcting other errors. In this paper, we address this serious limitation of existing approaches and improve strong neural encoder-decoder models by appropriately modeling wider contexts. We employ an auxiliary encoder that encodes previous sentences and incorporate the encoding in the decoder via attention and gating mechanisms. Our approach results in statistically significant improvements in overall GEC performance over strong baselines across multiple test sets. Analysis of our cross-sentence GEC model on a synthetic dataset shows high performance in verb tense corrections that require cross-sentence context.
Adversarial Attention Modeling for Multi-dimensional Emotion Regression
Authors: Suyang Zhu, Shoushan Li, Guodong Zhou
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: regression

In this paper, we propose a neural network-based approach, namely Adversarial Attention Network, to the task of multi-dimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text. Especially, to determine which words are valuable for a particular emotion dimension, an attention layer is trained to weight the words in an input sequence. Furthermore, adversarial training is employed between two attention layers to learn better word weights via a discriminator. In particular, a shared attention layer is incorporated to learn public word weights between two emotion dimensions. Empirical evaluation on the EMOBANK corpus shows that our approach achieves notable improvements in r-values on both EMOBANK Reader{'}s and Writer{'}s multi-dimensional emotion regression tasks in all domains over the state-of-the-art baselines.
Divide, Conquer and Combine: Hierarchical Feature Fusion Network with Local and Global Perspectives for Multimodal Affective Computing
Authors: Sijie Mai, Haifeng Hu, Songlong Xing
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

We propose a general strategy named {`}divide, conquer and combine{'} for multimodal fusion. Instead of directly fusing features at holistic level, we conduct fusion hierarchically so that both local and global interactions are considered for a comprehensive interpretation of multimodal embeddings. In the {`}divide{'} and {`}conquer{'} stages, we conduct local fusion by exploring the interaction of a portion of the aligned feature vectors across various modalities lying within a sliding window, which ensures that each part of multimodal embeddings are explored sufficiently. On its basis, global fusion is conducted in the {`}combine{'} stage to explore the interconnection across local interactions, via an Attentive Bi-directional Skip-connected LSTM that directly connects distant local interactions and integrates two levels of attention mechanism. In this way, local interactions can exchange information sufficiently and thus obtain an overall view of multimodal information. Our method achieves state-of-the-art performance on multimodal affective computing with higher efficiency.
Modeling Financial Analysts' Decision Making via the Pragmatics and Semantics of Earnings Calls
Authors: Katherine Keith, Am Stent, a
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Decision Making

Every fiscal quarter, companies hold earnings calls in which company executives respond to questions from analysts. After these calls, analysts often change their price target recommendations, which are used in equity re- search reports to help investors make deci- sions. In this paper, we examine analysts{'} decision making behavior as it pertains to the language content of earnings calls. We identify a set of 20 pragmatic features of analysts{'} questions which we correlate with analysts{'} pre-call investor recommendations. We also analyze the degree to which semantic and pragmatic features from an earnings call complement market data in predicting analysts{'} post-call changes in price targets. Our results show that earnings calls are moderately predictive of analysts{'} decisions even though these decisions are influenced by a number of other factors including private communication with company executives and market conditions. A breakdown of model errors indicates disparate performance on calls from different market sectors.
Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction
Authors: Debela Gemechu, Chris Reed
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Argument Mining, graph construction

This work presents an approach decomposing propositions into four functional components and identify the patterns linking those components to determine argument structure. The entities addressed by a proposition are target concepts and the features selected to make a point about the target concepts are aspects. A line of reasoning is followed by providing evidence for the points made about the target concepts via aspects. Opinions on target concepts and opinions on aspects are used to support or attack the ideas expressed by target concepts and aspects. The relations between aspects, target concepts, opinions on target concepts and aspects are used to infer the argument relations. Propositions are connected iteratively to form a graph structure. The approach is generic in that it is not tuned for a specific corpus and evaluated on three different corpora from the literature: AAEC, AMT, US2016G1tv and achieved an F score of 0.79, 0.77 and 0.64, respectively.
Transfer Capsule Network for Aspect Level Sentiment Classification
Authors: Zhuang Chen, Tieyun Qian
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Classification, General Classification, Sentence, Sentiment Analysis, Sentiment Classification, Transfer Learning

Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence towards an aspect. Due to the high cost in annotation, the lack of aspect-level labeled data becomes a major obstacle in this area. On the other hand, document-level labeled data like reviews are easily accessible from online websites. These reviews encode sentiment knowledge in abundant contexts. In this paper, we propose a Transfer Capsule Network (TransCap) model for transferring document-level knowledge to aspect-level sentiment classification. To this end, we first develop an aspect routing approach to encapsulate the sentence-level semantic representations into semantic capsules from both the aspect-level and document-level data. We then extend the dynamic routing approach to adaptively couple the semantic capsules with the class capsules under the transfer learning framework. Experiments on SemEval datasets demonstrate the effectiveness of TransCap.
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
Authors: Sheng Xu, Peifeng Li, Fang Kong, Qiaoming Zhu, Guodong Zhou
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Relation, Sentence, Tensor Networks

In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores.
Learning from Omission
Authors: Bill McDowell, Noah Goodman
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

Pragmatic reasoning allows humans to go beyond the literal meaning when interpret- ing language in context. Previous work has shown that such reasoning can improve the performance of already-trained language understanding systems. Here, we explore whether pragmatic reasoning during training can improve the quality of learned meanings. Our experiments on reference game data show that end-to-end pragmatic training produces more accurate utterance interpretation models, especially when data is sparse and language is complex.
Data Programming for Learning Discourse Structure
Authors: Sonia Badene, Kate Thompson, Jean-Pierre Lorr{\'e}, Nicholas Asher
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

This paper investigates the advantages and limits of data programming for the task of learning discourse structure. The data programming paradigm implemented in the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the {``}generative step{''} into probability distributions of the class labels given the training candidates. These results are later generalized using a discriminative model. Snorkel{'}s attractive promise to create a large amount of annotated data from a smaller set of training data by unifying the output of a set of heuristics has yet to be used for computationally difficult tasks, such as that of discourse attachment, in which one must decide where a given discourse unit attaches to other units in a text in order to form a coherent discourse structure. Although approaching this problem using Snorkel requires significant modifications to the structure of the heuristics, we show that weak supervision methods can be more than competitive with classical supervised learning approaches to the attachment problem.
End-to-end Deep Reinforcement Learning Based Coreference Resolution
Authors: Hongliang Fei, Xu Li, Dingcheng Li, Ping Li
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: coreference-resolution, Coreference Resolution, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent neural network models have significantly advanced the task of coreference resolution. However, current neural coreference models are usually trained with heuristic loss functions that are computed over a sequence of local decisions. In this paper, we introduce an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics. Specifically, we modify the state-of-the-art higher-order mention ranking approach in Lee et al. (2018) to a reinforced policy gradient model by incorporating the reward associated with a sequence of coreference linking actions. Furthermore, we introduce maximum entropy regularization for adequate exploration to prevent the model from prematurely converging to a bad local optimum. Our proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.
Coreference Resolution with Entity Equalization
Authors: Ben Kantor, Amir Globerson
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: coreference-resolution, Coreference Resolution

A key challenge in coreference resolution is to capture properties of entity clusters, and use those in the resolution process. Here we provide a simple and effective approach for achieving this, via an {``}Entity Equalization{''} mechanism. The Equalization approach represents each mention in a cluster via an approximation of the sum of all mentions in the cluster. We show how this can be done in a fully differentiable end-to-end manner, thus enabling high-order inferences in the resolution process. Our approach, which also employs BERT embeddings, results in new state-of-the-art results on the CoNLL-2012 coreference resolution task, improving average F1 by 3.6{\%}.
Just ``OneSeC'' for Producing Multilingual Sense-Annotated Data
Authors: Bianca Scarlini, Tommaso Pasini, Roberto Navigli
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Word Sense Disambiguation

The well-known problem of knowledge acquisition is one of the biggest issues in Word Sense Disambiguation (WSD), where annotated data are still scarce in English and almost absent in other languages. In this paper we formulate the assumption of One Sense per Wikipedia Category and present OneSeC, a language-independent method for the automatic extraction of hundreds of thousands of sentences in which a target word is tagged with its meaning. Our automatically-generated data consistently lead a supervised WSD model to state-of-the-art performance when compared with other automatic and semi-automatic methods. Moreover, our approach outperforms its competitors on multilingual and domain-specific settings, where it beats the existing state of the art on all languages and most domains. All the training data are available for research purposes at http://trainomatic.org/onesec.
Errudite: Scalable, Reproducible, and Testable Error Analysis
Authors: Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, Daniel Weld
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: counterfactual

Though error analysis is crucial to understanding and improving NLP models, the common practice of manual, subjective categorization of a small sample of errors can yield biased and incomplete conclusions. This paper codifies model and task agnostic principles for informative error analysis, and presents Errudite, an interactive tool for better supporting this process. First, error groups should be precisely defined for reproducibility; Errudite supports this with an expressive domain-specific language. Second, to avoid spurious conclusions, a large set of instances should be analyzed, including both positive and negative examples; Errudite enables systematic grouping of relevant instances with filtering queries. Third, hypotheses about the cause of errors should be explicitly tested; Errudite supports this via automated counterfactual rewriting. We validate our approach with a user study, finding that Errudite (1) enables users to perform high quality and reproducible error analyses with less effort, (2) reveals substantial ambiguities in prior published error analyses practices, and (3) enhances the error analysis experience by allowing users to test and revise prior beliefs.
Crowdsourcing and Aggregating Nested Markable Annotations
Authors: Chris Madge, Juntao Yu, Jon Chamberlain, Udo Kruschwitz, Silviu Paun, Massimo Poesio
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: coreference-resolution, Coreference Resolution, Entity Resolution, Text Segmentation

One of the key steps in language resource creation is the identification of the text segments to be annotated, or markables, which depending on the task may vary from nominal chunks for named entity resolution to (potentially nested) noun phrases in coreference resolution (or mentions) to larger text segments in text segmentation. Markable identification is typically carried out semi-automatically, by running a markable identifier and correcting its output by hand{--}which is increasingly done via annotators recruited through crowdsourcing and aggregating their responses. In this paper, we present a method for identifying markables for coreference annotation that combines high-performance automatic markable detectors with checking with a Game-With-A-Purpose (GWAP) and aggregation using a Bayesian annotation model. The method was evaluated both on news data and data from a variety of other genres and results in an improvement on F1 of mention boundaries of over seven percentage points when compared with a state-of-the-art, domain-independent automatic mention detector, and almost three points over an in-domain mention detector. One of the key contributions of our proposal is its applicability to the case in which markables are nested, as is the case with coreference markables; but the GWAP and several of the proposed markable detectors are task and language-independent and are thus applicable to a variety of other annotation scenarios.
OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs
Authors: Seungwhan Moon, Pararth Shah, Anuj Kumar, Rajen Subba
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Decoder, Knowledge Graphs

We study a conversational reasoning model that strategically traverses through a large-scale common fact knowledge graph (KG) to introduce engaging and contextually diverse entities and attributes. For this study, we collect a new Open-ended Dialog {\textless}-{\textgreater} KG parallel corpus called OpenDialKG, where each utterance from 15K human-to-human role-playing dialogs is manually annotated with ground-truth reference to corresponding entities and paths from a large-scale KG with 1M+ facts. We then propose the DialKG Walker model that learns the symbolic transitions of dialog contexts as structured traversals over KG, and predicts natural entities to introduce given previous dialog contexts via a novel domain-agnostic, attention-based graph path decoder. Automatic and human evaluations show that our model can retrieve more natural and human-like responses than the state-of-the-art baselines or rule-based models, in both in-domain and cross-domain tasks. The proposed model also generates a KG walk path for each entity retrieved, providing a natural way to explain conversational reasoning.
What Makes a Good Counselor? Learning to Distinguish between High-quality and Low-quality Counseling Conversations
Authors: Ver{\'o}nica P{\'e}rez-Rosas, Xinyi Wu, Kenneth Resnicow, Rada Mihalcea
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7

The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects of the collaboration process occurring during counseling conversations. Specifically, we address the differences between high-quality and low-quality counseling. Our approach examines participants{'} turn-by-turn interaction, their linguistic alignment, the sentiment expressed by speakers during the conversation, as well as the different topics being discussed. Our results suggest important language differences in low- and high-quality counseling, which we further use to derive linguistic features able to capture the differences between the two groups. These features are then used to build automatic classifiers that can predict counseling quality with accuracies of up to 88{\%}.
Towards Automating Healthcare Question Answering in a Noisy Multilingual Low-Resource Setting
Authors: Jeanne E. Daniel, Willie Brink, Ryan Eloff, Charles Copley
Date: 2019-07-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Answer Selection, Cross-Lingual Word Embeddings, Question Answering, Word Embeddings

We discuss ongoing work into automating a multilingual digital helpdesk service available via text messaging to pregnant and breastfeeding mothers in South Africa. Our anonymized dataset consists of short informal questions, often in low-resource languages, with unreliable language labels, spelling errors and code-mixing, as well as template answers with some inconsistencies. We explore cross-lingual word embeddings, and train parametric and non-parametric models on 90K samples for answer selection from a set of 126 templates. Preliminary results indicate that LSTMs trained end-to-end perform best, with a test accuracy of 62.13{\%} and a recall@5 of 89.56{\%}, and demonstrate that we can accelerate response time by several orders of magnitude.