Semantic Graphs for Generating Deep Questions (2004.12704v1)

Abstract: This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage. In order to capture the global structure of the document and facilitate reasoning, we propose a novel framework which first constructs a semantic-level graph for the input document and then encodes the semantic graph by introducing an attention-based GGNN (Att-GGNN). Afterwards, we fuse the document-level and graph-level representations to perform joint training of content selection and question decoding. On the HotpotQA deep-question centric dataset, our model greatly improves performance over questions requiring reasoning over multiple facts, leading to state-of-the-art performance. The code is publicly available at https://github.com/WING-NUS/SG-Deep-Question-Generation.

Semantic Graph Construction Using SRL and DP Techniques: A Detailed Examination

The paper presents an insightful exploration of semantic graph construction methodologies, employing Semantic Role Labeling (SRL) and Dependency Parsing (DP) to extract semantic relations from a given document. The innovative approach is applied to model the interrelations of entities and verbs within a text, facilitating improved semantic understanding and representation.

SRL-Based Semantic Graphs

The initial focus of the paper lies on SRL-based semantic graph construction. Semantic Role Labeling is utilized to decipher the predicate-argument structures of sentences, where each argument and associated verb is treated as a node. These nodes are then connected to formulate the semantic graph. The construction process commences with the application of a coreference resolution system to replace pronouns with their corresponding entities, enhancing node specificity.

Subsequently, the SRL algorithm is applied to generate a series of tuples, each comprising an argument, verb, and potential modifier. The nodes are iteratively linked based on similarity criteria, where string equivalence, containment, or significant overlap between word constituents define node similarity. The resultant semantic graph encapsulates rich relational semantics, that contribute meaningfully to understanding textual interplay.

DP-Based Semantic Graphs

The paper advances to the method of constructing semantic graphs through Dependency Parsing (DP). DP is an established syntactic analysis that delineates the structural relationships between words through a tree representation. The document aims to capture these syntactic dependencies, refine them through processes such as node pruning and merging, and ultimately incorporate them into a semantic graph.

The dependency parse is subjected to operations like node type identification by simplifying POS tags into intuitive categories such as verbs, nouns, and attributes. The subsequent pruning involves removing insignificant nodes, thereby reducing redundancy. Merging is carefully executed to condense nodes into aggregate semantic units, making the DP-based graphs suitably sparse but meticulously detailed.

Experimental Findings

Extensive experimentation is conducted to compare the effectiveness of SRL and DP-based methodologies. The findings reveal that both methodologies surpass traditional baselines regarding BLEU scores, reflecting in enhanced performance in tasks necessitating semantic comprehension. Notably, the DP-based graphs exhibit a slightly superior performance, with a recorded increment of 3.3% in the BLEU-4 metric compared to SRL-based graphs. This enhanced performance underscores the efficiency of DP-based techniques in capturing fine-grained semantic relationships which aid in improving downstream tasks.

Implications and Future Directions

The implications of this research are significant for NLP applications, particularly in automated text understanding and information extraction. Semantic graphs offer a promising approach to model complex relationships, enhancing the interpretability and robustness of NLP systems. Practically, these structured graphs could be instrumental in developing systems for machine comprehension, automated summarization, and question generation.

Theoretical implications suggest further exploration into integration methodologies that could combine the strengths of both SRL and DP approaches. It opens pathways for future advancements incorporating these graphs with deep learning models, enhancing AI's semantic comprehension capabilities. Such integration could lead to progress in multi-modal data processing given that future developments in AI are likely to focus on marrying linguistic, visual, and other contextual data streams.

In conclusion, the paper presents a substantive approach to constructing semantic graphs using SRL and DP, bolstering the semantic interpretability in NLP systems. The demonstration of measurable performance gains invites further research into refining these graph-based methodologies, ensuring they can be leveraged in complex and diverse AI-driven applications.