Weikang Yuan, Kaisong Song, Zhuoren Jiang, Junjie Cao, Yujie Zhang, Jun Lin, Kun Kuang, Ji Zhang, Xiaozhong Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Previous works on key information extraction from visually rich documents (VRDs) mainly focus on labeling the text within each bounding box (i.e., semantic entity), while the relations in-between are largely unexplored.In this paper, we adapt the popular dependency parsing model, the biaffine parser, to this entity relation extraction task.Being different from the original dependency parsing model which recognizes dependency relations between words, we identify relations between groups of words with layout information instead.We have compared different representations of the semantic entity, different VRD encoders, and different relation decoders.For the model training, we explore multi-task learning to combine entity labeling and relation extraction tasks; and for the evaluation, we conduct experiments on different datasets with filtering and augmentation.The results demonstrate that our proposed model achieves 65.96% F1 score on the FUNSD dataset.As for the realworld application, our model has been applied to the in-house customs data, achieving reliable performance in the production setting.

Abstract In this work, we present a phenomenon-oriented comparative analysis of the two dominant approaches in English Resource Semantic (ERS) parsing: classic, knowledge-intensive and neural, data-intensive models. To reflect state-of-the-art neural NLP technologies, a factorization-based parser is introduced that can produce Elementary Dependency Structures much more accurately than previous data-driven parsers. We conduct a suite of tests for different linguistic phenomena to analyze the grammatical competence of different parsers, where we show that, despite comparable performance overall, knowledge- and data-intensive models produce different types of errors, in a way that can be explained by their theoretical properties. This analysis is beneficial to in-depth evaluation of several representative parsing techniques and leads to new directions for parser development.

This paper is concerned with semantic parsing for English as a second language (ESL).Motivated by the theoretical emphasis on the learning challenges that occur at the syntaxsemantics interface during second language acquisition, we formulate the task based on the divergence between literal and intended meanings.We combine the complementary strengths of English Resource Grammar, a linguistically-precise hand-crafted deep grammar, and TLE, an existing manually annotated ESL UD-TreeBank with a novel reranking model.Experiments demonstrate that in comparison to human annotations, our method can obtain a very promising SemBanking quality.By means of the newly created corpus, we evaluate state-of-the-art semantic parsing as well as grammatical error correction models.The evaluation profiles the performance of neural NLP techniques for handling ESL data and suggests some research directions.

We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser.Our parser targets 1-endpoint-crossing, pagenumber-2 graphs which are a good fit to semantic dependency graphs, and utilizes an efficient dynamic programming algorithm for decoding.For disambiguation, the parser associates words with BiLSTM vectors and utilizes these vectors to assign scores to candidate dependencies.We conduct experiments on the data sets from Se-mEval 2015 as well as Chinese CCGBank.Our parser achieves very competitive results for both English and Chinese.To improve the parsing performance on cross-domain texts, we propose a data-oriented method to explore the linguistic generality encoded in English Resource Grammar, which is a precisionoriented, hand-crafted HPSG grammar, in an implicit way.Experiments demonstrate the effectiveness of our data-oriented method across a wide range of conditions.

We study the Maximum Subgraph problem in deep dependency parsing.We consider two restrictions to deep dependency graphs: (a) 1-endpoint-crossing and (b) pagenumber-2.Our main contribution is an exact algorithm that obtains maximum subgraphs satisfying both restrictions simultaneously in time O(n 5 ).Moreover, ignoring one linguistically-rare structure descreases the complexity to O(n 4 ).We also extend our quartic-time algorithm into a practical parser with a discriminative disambiguation model and evaluate its performance on four linguistic data sets used in semantic dependency parsing.

We model a dependency graph as a book, a particular kind of topological space, for semantic dependency parsing.The spine of the book is made up of a sequence of words, and each page contains a subset of noncrossing arcs.To build a semantic graph for a given sentence, we design new Maximum Subgraph algorithms to generate noncrossing graphs on each page, and a Lagrangian Relaxation-based algorithm to combine pages into a book.Experiments demonstrate the effectiveness of the book embedding framework across a wide range of conditions.Our parser obtains comparable results with a state-of-the-art transition-based parser.

We propose a new Maximum Subgraph algorithm for first-order parsing to 1endpoint-crossing, pagenumber-2 graphs.Our algorithm has two characteristics: (1) it separates the construction for noncrossing edges and crossing edges; (2) in a single construction step, whether to create a new arc is deterministic.These two characteristics make our algorithm relatively easy to be extended to incorporiate crossing-sensitive second-order features.We then introduce a new algorithm for quasi-second-order parsing.Experiments demonstrate that second-order features are helpful for Maximum Subgraph parsing.