NLP datasets are richer than just input-output pairs; rather, they carry causal relations between the input and output variables. In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y). As psychology studies show that language can affect emotion, different psychological processes are evoked when a person first makes a rating and then self-rationalizes their feeling in a review (where the sentiment causes the review, i.e., Y -> X), versus first describes their experience, and weighs the pros and cons to give a final rating (where the review causes the sentiment, i.e., X -> Y ). Furthermore, it is also a completely different psychological process if an annotator infers the original rating of the user by theory of mind (ToM) (where the review causes the rating, i.e., X -ToM-> Y ). In this paper, we verbalize these three causal mechanisms of human psychological processes of sentiment classification into three different causal prompts, and study (1) how differently they perform, and (2) what nature of sentiment classification data leads to agreement or diversity in the model responses elicited by the prompts. We suggest future work raise awareness of different causal structures in NLP tasks. Our code and data are at https://github.com/cogito233/psych-causal-prompt

In this paper, we propose document-level end-to-end sentiment analysis to efficiently understand aspect and review sentiment expressed in online reviews in a unified manner. In particular, we assume that star rating labels are a "coarse-grained synthesis" of aspect ratings across in the review. We propose a Distantly Supervised Pyramid Network (DSPN) to efficiently perform Aspect-Category Detection, Aspect-Category Sentiment Analysis, and Rating Prediction using only document star rating labels for training. By performing these three related sentiment subtasks in an end-to-end manner, DSPN can extract aspects mentioned in the review, identify the corresponding sentiments, and predict the star rating labels. We evaluate DSPN on multi-aspect review datasets in English and Chinese and find that with only star rating labels for supervision, DSPN can perform comparably well to a variety of benchmark models. We also demonstrate the interpretability of DSPN's outputs on reviews to show the pyramid structure inherent in document level end-to-end sentiment analysis.

We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github.com/afrisenti-semeval/afrisent-semeval-2023. AfriSenti-SemEval is a sentiment classification challenge in 14 African languages: Amharic, Algerian Arabic, Hausa, Igbo, Kinyarwanda, Moroccan Arabic, Mozambican Portuguese, Nigerian Pidgin, Oromo, Swahili, Tigrinya, Twi, Xitsonga, and Yor\`ub\'a (Muhammad et al., 2023), using data labeled with 3 sentiment classes. We present three subtasks: (1) Task A: monolingual classification, which received 44 submissions; (2) Task B: multilingual classification, which received 32 submissions; and (3) Task C: zero-shot classification, which received 34 submissions. The best performance for tasks A and B was achieved by NLNDE team with 71.31 and 75.06 weighted F1, respectively. UCAS-IIE-NLP achieved the best average score for task C with 58.15 weighted F1. We describe the various approaches adopted by the top 10 systems and their approaches.

We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i.e., N-tuples representing a mapping from named slots to spans of text) within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template's slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks -- 4-ary relation extraction on SciREX and template extraction on MUC-4 -- as well as a strong baseline on the new BETTER Granular task.

Information Extraction from visually rich documents is a challenging task that has gained a lot of attention in recent years due to its importance in several document-control based applications and its widespread commercial value. The majority of the research work conducted on this topic to date follow a two-step pipeline. First, they read the text using an off-the-shelf Optical Character Recognition (OCR) engine, then, they extract the fields of interest from the obtained text. The main drawback of these approaches is their dependence on an external OCR system, which can negatively impact both performance and computational speed. Recent OCR-free methods were proposed to address the previous issues. Inspired by their promising results, we propose in this paper an OCR-free end-to-end information extraction model named DocParser. It differs from prior end-to-end approaches by its ability to better extract discriminative character features. DocParser achieves state-of-the-art results on various datasets, while still being faster than previous works.

Advances in the Visually-rich Document Understanding (VrDU) field and particularly the Key-Information Extraction (KIE) task are marked with the emergence of efficient Transformer-based approaches such as the LayoutLM models. Despite the good performance of KIE models when fine-tuned on public benchmarks, they still struggle to generalize on complex real-life use-cases lacking sufficient document annotations. Our research highlighted that KIE standard benchmarks such as SROIE and FUNSD contain significant similarity between training and testing documents and can be adjusted to better evaluate the generalization of models. In this work, we designed experiments to quantify the information redundancy in public benchmarks, revealing a 75% template replication in SROIE's official test set and 16% in FUNSD's. We also proposed resampling strategies to provide benchmarks more representative of the generalization ability of models. We showed that models not suited for document analysis struggle on the adjusted splits dropping on average 10,5% F1 score on SROIE and 3.5% on FUNSD compared to multi-modal models dropping only 7,5% F1 on SROIE and 0.5% F1 on FUNSD. Keywords: Visually-rich Document Understanding Key Information Extraction Named Entity Recognition Generalization Assessment.

Sentence-by-sentence information extraction from long documents is an exhausting and error-prone task. As the indicator of document skeleton, catalogs naturally chunk documents into segments and provide informative cascade semantics, which can help to reduce the search space. Despite their usefulness, catalogs are hard to be extracted without the assist from external knowledge. For documents that adhere to a specific template, regular expressions are practical to extract catalogs. However, handcrafted heuristics are not applicable when processing documents from different sources with diverse formats. To address this problem, we build a large manually annotated corpus, which is the first dataset for the Catalog Extraction from Documents (CED) task. Based on this corpus, we propose a transition-based framework for parsing documents into catalog trees. The experimental results demonstrate that our proposed method outperforms baseline systems and shows a good ability to transfer. We believe the CED task could fill the gap between raw text segments and information extraction tasks on extremely long documents. Data and code are available at \url{https://github.com/Spico197/CatalogExtraction}

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain -- Reddit) for multi-level classification into Sexist or not Sexist, and other subsequent sub-classifications of the sexist data. We also use synthetic classification of unlabelled dataset and intermediary class information to maximize the performance of our models. We submitted a system in Task A, and it ranked 49th with F1-score of 0.82. This result showed to be competitive as it only under-performed the best system by 0.052% F1-score.

This paper describes our system developed for the SemEval-2023 Task 12 "Sentiment Analysis for Low-resource African Languages using Twitter Dataset". Sentiment analysis is one of the most widely studied applications in natural language processing. However, most prior work still focuses on a small number of high-resource languages. Building reliable sentiment analysis systems for low-resource languages remains challenging, due to the limited training data in this task. In this work, we propose to leverage language-adaptive and task-adaptive pretraining on African texts and study transfer learning with source language selection on top of an African language-centric pretrained language model. Our key findings are: (1) Adapting the pretrained model to the target language and task using a small yet relevant corpus improves performance remarkably by more than 10 F1 score points. (2) Selecting source languages with positive transfer gains during training can avoid harmful interference from dissimilar languages, leading to better results in multilingual and cross-lingual settings. In the shared task, our system wins 8 out of 15 tracks and, in particular, performs best in the multilingual evaluation.

Our contribution to the 2023 AfriSenti-SemEval shared task 12: Sentiment Analysis for African Languages, provides insight into how a multilingual large language model can be a resource for sentiment analysis in languages not seen during pretraining. The shared task provides datasets of a variety of African languages from different language families. The languages are to various degrees related to languages used during pretraining, and the language data contain various degrees of code-switching. We experiment with both monolingual and multilingual datasets for the final fine-tuning, and find that with the provided datasets that contain samples in the thousands, monolingual fine-tuning yields the best results.