C ofe N et: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (2022)

Abstract: Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Co ntext and F ormer-Label E nhanced Net () for quotation extraction. is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (and ) and one proprietary dataset (), we show that our achieves state-of-the-art performance on complicated quotation extraction.

## introduction quotation extraction aims to extract quotations from written text @xcite . for example, given one instance shown in figure 1 , we extract the quotation with source: some democrats, cue: privately express, and content: reservations about .... as a point of view, quotations provide opinions of the speaker, which is important for analyzing the speaker's stand. in general, quotation extraction is the first step before any further analysis, e.g., speaker stand detection. in this paper, we focus on the extraction of the three quotation components. as illustrated in the above example, the extraction of content component in a quotation is complicated and difficult due to three reasons: variable length, unclear boundary, and indistinguishable components. specifically, the length of content can be over 10, or even more than 50 tokens. moreover, content does not come with a regular pattern, which not only leads to a more unclear boundary of itself, but also affects the estimation of source and cue. for example, content in a quotation can be a complete instance with subject, predicate, and object. it is therefore hard to distinguish a noun (subject or object) representing the source or a part of content. difficulty also exists in recognition of cue when tackling with a predicate, e.g., verb. thus, as content may contain another quotation, such a nesting structure further increases the difficulty of extracting quotations. many existing solutions for quotation extraction are rule-based methods @xcite @xcite . generally, quotations include direct quotations and indirect quotations. quotation marks and their variants are clear; thus content can be extracted by using regular expressions. however, not all quoted texts are quotations. meanwhile, not all quotations are quoted. another popular rule-based approach is to recognize cue words, e.g., speak(s). similarly, not all cue words are related to quotations and vice versa. for both approaches, after recognizing content or cue, they usually search for the nearby noun as source. in short, rule-based methods only cover limited cases, leading to serious low recall problems. quotation extraction has also been formulated as a sequence labeling task. @xcite ; @xcite directly adopt sequence labeling for quotation extraction. however, these solutions ignore the traits of quotations where lengths of quotation components are variable and structures of content are complicated. in general, source and cue components are short, e.g., ≤ 3 tokens. however, content usually is over 10 tokens, or even more. further, the complicated structure of content greatly reduces the performance of content extraction for sequence-labeling-based solutions. in this paper, we propose context and former-label enhanced net (cofenet) for quotation extraction. cofenet is a novel architecture to extract quotations with variable-length and complicatedstructured components. our model is also capable of extracting both direct and indirect quotations. cofenet extracts quotations by utilizing dependent relations between sequenced texts. the model contains three components, i.e., text encoder, enhanced cell, and label assigner. given a piece of text, the encoder encodes the instance and outputs the encoded hidden vectors. we design the enhanced cell module to study semantic representations of variable-length components with the utilization of contextual information. specifically, the enhanced cell (i) uses a composer layer to enhance the input with the former labels (which are predicted by the former cells), the former words, the current word, and the latter words encoded by the encoder; and (ii) uses a gate layer and an attention layer to control and attend the corresponding input when predicting the label of the current word, at the level of element and vector respectively. experimental results on two public datasets (i.e., pol-near and riqua) and one proprietary dataset (i.e., politicszh) show that our cofenet achieves stateof-the-art performance on complicated quotation extraction. ## related work at first glance, quotation detection is a kind of "triplet" extraction, making the task similar to another two tasks, open information extraction @xcite and semantic role labeling @xcite ). however, these three tasks have different focuses. arguments extracted by semantic role labeling are event-related factors. openie aims to output a structured representation of an instance in the form of binary or n-ary tuples, each of which consists of a predicate and several arguments. the extracted text spans in both tasks are typically short and less complicated, compared to the content in quotations. because content extraction is the key challenge in quotation extraction, we will not further elaborate on semantic role labeling and openie. prior work on quotation extraction can be grouped into rule-based and sequence labeling methods. ## experiment we now evaluate the proposed cofenet on two public datasets (i.e., polnear and riqua), and one proprietary dataset (i.e., politicszh) against baselines. the implementation details and parameter settings are presented in appendix a. on all datasets, we train the model with the training set, tune hyperparameters on the validation set, and report performance on the test set. ## conclusion and future work in this study, we design the cofenet model for quotation extraction with variable-length span and complicated structure. the key idea of cofenet model is to use gate and attention mechanisms to control the important information including former labels, former words, current word and latter words at the element and vector levels. experiments show that the proposed model achieves the state-of-theart performance on two public datasets polnear and riqua and one proprietary dataset politicszh. for quotation analysis, the extraction of quotation components is the first step. in our study, we split a long text into short texts to ensure that one instance contains one source, one cue and one content. thus the recognition of quotation triplets from long text (e.g., across instance) is one important future work. another important direction is to go deep into the nesting phenomenon, which makes the recognition harder. we use adam (kingma and ba, 2015) as our optimization method. cofenet is implemented on pytorch (version 1.2.0). nltk is used to segment text. for bert model, we invoke the pytorchtransformers package (version 1.2.0).