pwc-archive/papers-with-abstracts huggingface dataset:
Taxy.io@FinTOC-2020: Multilingual Document Structure Extraction using Transfer Learning
Authors: Frederic Haase, Steffen Kirchhoff
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Transfer Learning

In this paper we describe our system submitted to the FinTOC-2020 shared task on financial doc- ument structure extraction. We propose a two-step approach to identify titles in financial docu- ments and to extract their table of contents (TOC). First, we identify text blocks as candidates for titles using unsupervised learning based on character-level information of each document. Then, we apply supervised learning on a self-constructed regression task to predict the depth of each text block in the document structure hierarchy using transfer learning combined with document features and layout features. It is noteworthy that our single multilingual model performs well on both tasks and on different languages, which indicates the usefulness of transfer learning for title detection and TOC generation. Moreover, our approach is independent of the presence of actual TOC pages in the documents. It is also one of the few submissions to the FinTOC-2020 shared task addressing both subtasks in both languages, English and French, with one single model.
DNLP@FinTOC’20: Table of Contents Detection in Financial Documents
Authors: Dijana Kosmajac, Stacey Taylor, Mozhgan Saeidi
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

Title Detection and Table of Contents Generation are important components in detecting document structure. In particular, these two elements serve to provide the skeleton of the document, providing users with an understanding of organization, as well as the relevance of information, and where to find information within the document. Here, we show that using tesseract with Levenstein distance, a feature set inspired by Alk et al., we were able to correctly classify the title to an F1 measure 0.73 and 0.87, and the table-of-contents to a harmonic mean of 0.36 and 0.39, in English and French respectively. Our methodology works with both PDF and scanned documents, giving it a wide range of applicability within the document engineering and storage domains.
Daniel@FinTOC’2 Shared Task: Title Detection and Structure Extraction
Authors: Emmanuel Giguet, Gaël Lejeune, Jean-Baptiste Tanguy
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

We present our contributions for the 2020 FinTOC Shared Tasks: Title Detection and Table of Contents Extraction. For the Structure Extraction task, we propose an approach that combines information from multiple sources: the table of contents, the wording of the document, and lexical domain knowledge. For the title detection task, we compare surface features to character-based features on various training configurations. We show that title detection results are very sensitive to the kind of training dataset used.
A Computational Analysis of Financial and Environmental Narratives within Financial Reports and its Value for Investors
Authors: Felix Armbrust, Henry Schäfer, Roman Klinger
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

Public companies are obliged to include financial and non-financial information within their cor- porate filings under Regulation S-K, in the United States (SEC, 2010). However, the requirements still allow for manager’s discretion. This raises the question to which extent the information is actually included and if this information is at all relevant for investors. We answer this question by training and evaluating an end-to-end deep learning approach (based on BERT and GloVe embeddings) to predict the financial and environmental performance of the company from the “Management’s Discussion and Analysis of Financial Conditions and Results of Operations” (MD&A) section of 10-K (yearly) and 10-Q (quarterly) filings. We further analyse the mediating effect of the environmental performance on the relationship between the company’s disclosures and financial performance. Hereby, we address the results of previous studies regarding environ- mental performance. We find that the textual information contained within the MD&A section does not allow for conclusions about the future (corporate) financial performance. However, there is evidence that the environmental performance can be extracted by natural language processing methods.
Information Extraction from Federal Open Market Committee Statements
Authors: Oana Frunza
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

We present a novel approach to unsupervised information extraction by identifying and extracting relevant concept-value pairs from textual data. The system’s building blocks are domain agnostic, making it universally applicable. In this paper, we describe each component of the system and how it extracts relevant economic information from U.S. Federal Open Market Committee (FOMC) statements. Our methodology achieves an impressive 96% accuracy for identifying relevant information for a set of seven economic indicators: household spending, inflation, unemployment, economic activity, fixed in-vestment, federal funds rate, and labor market.
Mitigating Silence in Compliance Terminology during Parsing of Utterances
Authors: Esme Manandise, Conrad de Peuter
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

This paper reports on an approach to increase multi-token-term recall in a parsing task. We use a compliance-domain parser to extract, during the process of parsing raw text, terms that are unlisted in the terminology. The parser uses a similarity measure (Generalized Dice Coefficient) between listed terms and unlisted term candidates to (i) determine term status, (ii) serve putative terms to the parser, (iii) decrease parsing complexity by glomming multi-tokens as lexical singletons, and (iv) automatically augment the terminology after parsing of an utterance completes. We illustrate a small experiment with examples from the tax-and-regulations domain. Bootstrapping the parsing process to detect out- of-vocabulary terms at runtime increases parsing accuracy in addition to producing other benefits to a natural-language-processing pipeline, which translates arithmetic calculations written in English into computer-executable operations.
Hierarchical summarization of financial reports with RUNNER
Authors: Marina Litvak, Natalia Vanetik, Zvi Puchinsky
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

With the constantly growing amount of information, the need arises to automatically summarize this written information. One of the challenges in the summary is that it’s difficult to generalize. For example, summarizing a news article is very different from summarizing a financial earnings report. This paper reports an approach for summarizing financial texts, which are different from the documents from other domains at least in three parameters: length, structure, and format. Our approach considers these parameters, it is adapted to hierarchical structure of sections, document length, and special “language”. The approach builds an hierarchical summary, visualized as a tree with summaries under different discourse topics. The approach was evaluated using extrinsic and intrinsic automated evaluations, which are reported in this paper. As all participants of the Financial Narrative Summarisation (FNS 2020) shared task, we used FNS2020 dataset for evaluations.
Predicting Modality in Financial Dialogue
Authors: Kilian Theil, Heiner Stuckenschmidt
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12

In this paper, we perform modality prediction in financial dialogue. To this end, we introduce a new dataset and develop a binary classifier to detect strong or weak modal answers depending on surface, lexical, and semantic representations of the preceding question and financial features. To do so, we contrast different algorithms, feature categories, and fusion methods. Perhaps counter-intuitively, our results indicate that the strongest features for the given task are financial uncertainty measures such as market and individual firm risk.
Extracting Fine-Grained Economic Events from Business News
Authors: Gilles Jacobs, Veronique Hoste
Date: 2020-12-01 00:00:00
Proceeding: FNP (COLING) 2020 12
Tasks: Event Detection, Event Extraction

Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50% are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap.
Research on Discourse Parsing: from the Dependency View
Authors: Sujian Li
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Discourse Parsing, Reading Comprehension

Discourse parsing aims to comprehensively acquire the logical structure of the whole text which may be helpful to some downstream applications such as summarization, reading comprehension, QA and so on. One important issue behind discourse parsing is the representation of discourse structure. Up to now, many discourse structures have been proposed, and the correponding parsing methods are designed, promoting the development of discourse research. In this paper, we mainly introduce our recent discourse research and its preliminary application from the dependency view.
A Review of Discourse-level Machine Translation
Authors: Xiaojun Zhang
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Machine Translation, NMT, Sentence, Translation

Machine translation (MT) models usually translate a text at sentence level by considering isolated sentences, which is based on a strict assumption that the sentences in a text are independent of one another. However, the fact is that the texts at discourse level have properties going beyond individual sentences. These properties reveal texts in the frequency and distribution of words, word senses, referential forms and syntactic structures. Dissregarding dependencies across sentences will harm translation quality especially in terms of coherence, cohesion, and consistency. To solve these problems, several approaches have previously been investigated for conventional statistical machine translation (SMT). With the fast growth of neural machine translation (NMT), discourse-level NMT has drawn increasing attention from researchers. In this work, we review major works on addressing discourse related problems for both SMT and NMT models with a survey of recent trends in the fields.
A Test Suite for Evaluating Discourse Phenomena in Document-level Neural Machine Translation
Authors: Xinyi Cai, Deyi Xiong
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Machine Translation, NMT, Translation

The need to evaluate the ability of context-aware neural machine translation (NMT) models in dealing with specific discourse phenomena arises in document-level NMT. However, test sets that satisfy this need are rare. In this paper, we propose a test suite to evaluate three common discourse phenomena in English-Chinese translation: pronoun, discourse connective and ellipsis where discourse divergences lie across the two languages. The test suite contains 1,200 instances, 400 for each type of discourse phenomena. We perform both automatic and human evaluation with three state-of-the-art context-aware NMT models on the proposed test suite. Results suggest that our test suite can be used as a challenging benchmark test bed for evaluating document-level NMT. The test suite will be publicly available soon.
Comparison of the effects of attention mechanism on translation tasks of different lengths of ambiguous words
Authors: Yue Hu, Jiahao Qin, Zemeiqi Chen, Jingshi Zhou, Xiaojun Zhang
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Decoder, Machine Translation, NMT, Translation, Word Sense Disambiguation

In recent years, attention mechanism has been widely used in various neural machine translation tasks based on encoder decoder. This paper focuses on the performance of encoder decoder attention mechanism in word sense disambiguation task with different text length, trying to find out the influence of context marker on attention mechanism in word sense disambiguation task. We hypothesize that attention mechanisms have similar performance when translating texts of different lengths. Our conclusion is that the alignment effect of attention mechanism is magnified in short text translation tasks with ambiguous nouns, while the effect of attention mechanism is far less than expected in long-text tasks, which means that attention mechanism is not the main mechanism for NMT model to feed WSD to integrate context information. This may mean that attention mechanism pays more attention to ambiguous nouns than context markers. The experimental results show that with the increase of text length, the performance of NMT model using attention mechanism will gradually decline.
Context-Aware Word Segmentation for Chinese Real-World Discourse
Authors: Kaiyu Huang, Junpeng Liu, Jingxiang Cao, Degen Huang
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Articles, Chinese Word Segmentation, Sentence

Previous neural approaches achieve significant progress for Chinese word segmentation (CWS) as a sentence-level task, but it suffers from limitations on real-world scenario. In this paper, we address this issue with a context-aware method and optimize the solution at document-level. This paper proposes a three-step strategy to improve the performance for discourse CWS. First, the method utilizes an auxiliary segmenter to remedy the limitation on pre-segmenter. Then the context-aware algorithm computes the confidence of each split. The maximum probability path is reconstructed via this algorithm. Besides, in order to evaluate the performance in discourse, we build a new benchmark consisting of the latest news and Chinese medical articles. Extensive experiments on this benchmark show that our proposed method achieves a competitive performance on a document-level real-world scenario for CWS.
Neural Abstractive Multi-Document Summarization: Hierarchical or Flat Structure?
Authors: Ye Ma, Lu Zong
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Decoder, Document Summarization, Multi-Document Summarization, Word Embeddings

With regards to WikiSum (CITATION) that empowers applicative explorations of Neural Multi-Document Summarization (MDS) to learn from large scale dataset, this study develops two hierarchical Transformers (HT) that describe both the cross-token and cross-document dependencies, at the same time allow extended length of input documents. By incorporating word- and paragraph-level multi-head attentions in the decoder based on the parallel and vertical architectures, the proposed parallel and vertical hierarchical Transformers (PHT &VHT) generate summaries utilizing context-aware word embeddings together with static and dynamics paragraph embeddings, respectively. A comprehensive evaluation is conducted on WikiSum to compare PHT &VHT with established models and to answer the question whether hierarchical structures offer more promising performances than flat structures in the MDS task. The results suggest that our hierarchical models generate summaries of higher quality by better capturing cross-document relationships, and save more memory spaces in comparison to flat-structure models. Moreover, we recommend PHT given its practical value of higher inference speed and greater memory-saving capacity.
Intent Segmentation of User Queries Via Discourse Parsing
Authors: Vicente Ivan Sanchez Carmona, Yibing Yang, Ziyue Wen, Ruosen Li, Xiaohua Wang, Changjian Hu
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: BIG-bench Machine Learning, Chatbot, Discourse Parsing, Segmentation

In this paper, we explore a new approach based on discourse analysis for the task of intent segmentation. Our target texts are user queries from a real-world chatbot. Our results show the feasibility of our approach with an F1-score of 82.97 points, and some advantages and disadvantages compared to two machine learning baselines: BERT and LSTM+CRF.
Bridging Question Answering and Discourse The case of Multi-Sentence Questions
Authors: Bonnie Webber
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Community Question Answering, Question Answering, Sentence

In human question-answering (QA), questions are often expressed in the form of multiple sentences. One can see this in both spoken QA interactions, when one person asks a question of another, and written QA, such as are found on-line in FAQs and in what are called ”Community Question-Answering Forums”. Computer-based QA has taken the challenge of these ”multi-sentence questions” to be that of breaking them into an appropriately ordered sequence of separate questions, with both the previous questions and their answers serving as context for the next question. This can be seen, for example, in two recent workshops at AAAI called ”Reasoning for Complex QA” [https://rcqa-ws.github.io/program/]. We claim that, while appropriate for some types of ”multi-sentence questions” (MSQs), it is not appropriate for all, because they are essentially different types of discourse. To support this claim, we need to provide evidence that: • different types of MSQs are answered differently in written or spoken QA between people; • people can (and do) distinguish these different types of MSQs; • systems can be made to both distinguish different types of MSQs and provide appropriate answers.
Component Sharing in English and Chinese Clause Complex
Authors: Shili Ge, Xiaoping Lin, Rou Song
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Machine Translation, Translation

NT Clause Complex Framework defines a clause complex as a combination of NT clauses through component sharing and logic-semantic relationship. This paper clarifies the existence of component sharing mechanism in both English and Chinese clause complexes, illustrates the differences in component sharing between the two languages, and introduces a formal annotation scheme to represent clause-complex level structural transformations. Under the guidance of the annotation scheme, the English-Chinese Clause Alignment Corpus is built. It is believed that this corpus will aid comparative linguistic studies, translation studies and machine translation studies by providing abundant formal and computable samples for English-Chinese structural transformations on the clause complex level.
Referential Cohesion A Challenge for Machine Translation Evaluation
Authors: Christian Hardmeier
Date: 2020-12-01 00:00:00
Proceeding: AACL (iwdp) 2020 12
Tasks: Machine Translation, Specificity, Translation

Connected texts are characterised by the presence of linguistic elements relating to shared referents throughout the text. These elements together form a structure that lends cohesion to the text. The realisation of those cohesive structures is subject to different constraints and varying preferences in different languages. We regularly observe mismatches of cohesive structures across languages in parallel texts. This can be a result of either a divergence of language-internal constraints or of effects of the translation process. As fully automatic high-quality MT is starting to look achievable, the question arises how cohesive elements should be handled in MT evaluation, since the common assumption of 1:1 correspondence between referring expressions is a poor match for what we find in corpus data. Focusing on the translation of pronouns, I discuss different approaches to evaluating a particular type of cohesive elements in MT output and the trade-offs they make between evaluation cost, validity, specificity and coverage. I suggest that a meaningful evaluation of cohesive structures in translation is difficult to achieve simply by appealing to the intuition of human annotators, but requires a more structured approach that forces us to make up our minds about the standards we expect the translation output to adhere to.
Overview of the 7th Workshop on Asian Translation
Authors: Toshiaki Nakazawa, Hideki Nakayama, Chenchen Ding, Raj Dabre, Shohei Higashiyama, Hideya Mino, Isao Goto, Win Pa Pa, Anoop Kunchukuttan, Shantipriya Parida, Ondřej Bojar, Sadao Kurohashi
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Translation

This paper presents the results of the shared tasks from the 7th workshop on Asian translation (WAT2020). For the WAT2020, 20 teams participated in the shared tasks and 14 teams submitted their translation results for the human evaluation. We also received 12 research paper submissions out of which 7 were accepted. About 500 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.
An Effective Optimization Method for Neural Machine Translation: The Case of English-Persian Bilingually Low-Resource Scenario
Authors: Benyamin Ahmadnia, Raul Aranovich
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, NMT, Translation

In this paper, we propose a useful optimization method for low-resource Neural Machine Translation (NMT) by investigating the effectiveness of multiple neural network optimization algorithms. Our results confirm that applying the proposed optimization method on English-Persian translation can exceed translation quality compared to the English-Persian Statistical Machine Translation (SMT) paradigm.
Transformer-based Double-token Bidirectional Autoregressive Decoding in Neural Machine Translation
Authors: Kenji Imamura, Eiichiro Sumita
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Decoder, Machine Translation, Sentence, Translation

This paper presents a simple method that extends a standard Transformer-based autoregressive decoder, to speed up decoding. The proposed method generates a token from the head and tail of a sentence (two tokens in total) in each step. By simultaneously generating multiple tokens that rarely depend on each other, the decoding speed is increased while the degradation in translation quality is minimized. In our experiments, the proposed method increased the translation speed by around 113%-155% in comparison with a standard autoregressive decoder, while degrading the BLEU scores by no more than 1.03. It was faster than an iterative non-autoregressive decoder in many conditions.
Translation of New Named Entities from English to Chinese
Authors: Zizheng Zhang, Tosho Hirasawa, Wei Houjing, Masahiro Kaneko, Mamoru Komachi
Date: 2020-12-01 00:00:00
Proceeding: AACL (WAT) 2020 12
Tasks: Translation

New things are being created and new words are constantly being added to languages worldwide. However, it is not practical to translate them all manually into a new foreign language. When translating from an alphabetic language such as English to Chinese, appropriate Chinese characters must be assigned, which is particularly costly compared to other language pairs. Therefore, we propose a task of generating and evaluating new translations from English to Chinese focusing on named entities. We defined three criteria for human evaluation—fluency, adequacy of pronunciation, and adequacy of meaning—and constructed evaluation data based on these definitions. In addition, we built a baseline system and analyzed the output of the system.