Gap Sentences Generation (GSG)

• Gap Sentences Generation is an automated method for producing sentences with deliberate gaps, enabling tasks like NLG, parsing, and summarization.
• Techniques range from corpus-driven template extraction with genetic algorithms to neural prototype-based editing and transformer-based infilling, yielding improved metrics such as BLEU and F1 scores.
• GSG bridges semantic discontinuities through approaches like dependency graph reconstruction, exemplar-guided gap-filling, and cross-modal embedding alignment to enhance language processing.

Gap Sentences Generation (GSG) comprises a family of methodologies for constructing, manipulating, or inferring sentences with deliberate omissions—gaps—whether for natural language generation (NLG), parsing elided structures, infilling via contextual completion, educational exercise creation, summarization pre-training, or bridging semantic or modal discontinuities in cross-modal contexts.

1. Definition and Frameworks

GSG refers to the automated process of producing sentences containing explicit gaps (placeholders or regions of omission) or reconstructing elided information in textual input. The construction or reconstruction of gap sentences can serve multiple purposes: supporting template-based NLG, enabling unsupervised or self-supervised learning objectives, facilitating infilling tasks, enhancing parsing representations, driving exercise generation in education, and bridging information or semantic discontinuities. Architectures for GSG range from corpus-driven template extraction coupled with search algorithms (Bhatnagar et al., 2016), attention-based neural editing models (Guu et al., 2017), dependency graph manipulation for parsing (Schuster et al., 2018), transformer-based infilling (Huang et al., 2019), pre-training via masked sentence restoration (Zhang et al., 2019), neural conditionally-controlled span selection (Bitew et al., 2023), answer-focused question synthesis (Rabin et al., 2023), and cross-modal embedding alignment for retrieval-augmented systems (Yadav et al., 30 Oct 2024).

2. Corpus-Driven Template Extraction and Genetic Combination

The template-driven approach to GSG (Bhatnagar et al., 2016) models sentences as linear combinations of locally grammatical templates extracted from a linguistic corpus. Chunkers (e.g., CRFChunker) produce sub-sentential sequences, with gaps annotated via linguistic factors such as POS tags. Factoring is controlled via frequency-based thresholds, abstracting tokens for subsequent gap filling. Genetic Algorithms (GAs) structure sentence generation as the evolution of chromosomes: each chromosome is an ordered template sequence, with millions of unique templates in the population. GA operators include tournament selection (selecting the fittest among sampled candidates), crossover via template juxtaposition conditioned on trigram probability at junctions (with modified Kneser-Ney smoothing), and mutation by full chromosome replacement to promote diversity and control sentence length. Fitness is quantified by a 5-gram model over chunk tag sequences normalized to target length. Baseline implementations maintain populations of 1 million chromosomes across 100 generations, yielding gapped texts such as “the NN UH 1 EX EX” and fitness stabilization near 0.18. This approach enables unsupervised exploration of the template space; however, limitations remain in handling punctuation and long-distance dependencies.

Component	Technique	Notes
Extraction	Chunking + Factoring	POS tagging for gaps; abstraction via thresholds
Combination	Genetic Algorithm	Selection, crossover (NGram-guided), mutation
Fitness	Chunk tag 5-gram model	Modified Kneser-Ney smoothing; length normalization

3. Neural Editing and Prototype-Based Generation

Prototype-then-edit methods for GSG (Guu et al., 2017) initiate sentence generation by sampling a prototype sentence from the corpus, subsequently edited by a neural attention-based sequence-to-sequence editor conditioned on a latent “edit vector” $z$. Prototype selection is constrained to neighbors with high lexical overlap using Jaccard distance, and the edit process uses a vector $z$ representing the semantic difference—computed via embedding summation of inserted and deleted words, perturbed by von Mises–Fisher noise to maintain cosine similarity structure. The generative model $p(x) = \sum_{x'} p(x|x') p(x')$ is optimized via variational inference with an inverse editor $q(z | x', x)$ and an ELBO objective. This method improves perplexity (by up to 13 points in some datasets) and supports contextually diverse, plausible outputs; the latent edit vector supports interpretable semantic control and sentence-level analogies, positioning prototype-based GSG as an effective means of controlled text modification for gap filling.

4. Parsing and Reconstruction of Elided Sentences

Parsing-driven GSG targets the recovery of non-overt predicates in gapped constructions (Schuster et al., 2018). Two graph-centric methodologies operate over Universal Dependencies (UD):

1. Composite Relations: Labels concatenate dependency paths from the surface to elided material (e.g., “conj›obj”), which are then atomized in postprocessing to insert copy nodes representing missing predicates.
2. Orphan Procedure: Initial parsing promotes a remnant as head, linking others with “orphan” relations. Subsequent alignment (using Needleman–Wunsch) matches argument lists from full and gapped conjuncts, guided by embedding similarity and POS tag compatibility. Copy nodes represent reconstructed predicates, and dependency reattachment is performed.

Enhanced UD graphs feature explicit nodes and edges for elided material, supporting canonicalization for downstream tasks (e.g., relation extraction, open IE). Oracle experiments yield near-perfect predicate reconstruction; end-to-end parsing achieves 32–34% sentence-level accuracy. Cross-linguistic applicability is demonstrated for Swedish, achieving 98.18% labeled precision/recall (on cleaned annotation), with broad generalizability for languages with UD treebanks.

5. Gap Sentence Infilling with Transformer Architectures

Sentence infilling, or missing sentence generation (Huang et al., 2019), is structurally decomposed into understanding (representation learning), discourse planning, and generation. Understanding is realized via a BERT-based denoising autoencoder, producing sentence embeddings. Discourse planning employs a sentence-level transformer (with positional encodings), predicting latent features for the missing position via cosine similarity loss. Generation remaps the predicted feature to text using a GPT-2-based decoder. This pipeline, when fine-tuned, demonstrates higher BLEU and lexical diversity scores compared to token-level infilling baselines—e.g., improved accuracy on TripAdvisor datasets—and is validated by human evaluation for fluency, informativeness, and coherence.

6. Gap-Sentence Based Pre-training for Summarization

PEGASUS (Zhang et al., 2019) introduces GSG as a pre-training objective for Transformer encoder-decoder models: pivotal sentences (“gap sentences”) are masked out from a document (via random, lead, or principal selection strategies—principal often leveraging ROUGE-1 overlap) and the model is trained to reconstruct them. The objective is to maximize $\log P(T | X; \theta)$, where $X$ is the input with masked sentences and $T$ is the concatenated gaps. PEGASUS demonstrates state-of-the-art ROUGE performance on 12 summarization tasks (news, science, stories, patents, bills) and retains effectiveness in low-resource settings. Human evaluation confirms summary quality matching human performance on several datasets, with the GSG objective closely mirroring abstractive summarization needs. This approach illustrates the advantages of aligning pre-training with intended downstream tasks and suggests broader applicability in generative objectives that require restoration of omitted information.

Strategy	Description	Application Context
Principal	Masks high-ROUGE sentences	Abstractive summarization
Lead	Masks initial sentences	News summarization
Random	Masks sentences uniformly	Baseline/Control

7. Conditional Gap-Filling in Educational Exercise Generation

Automatic exercise creation for language learning leverages GSG via example-aware span detection (Bitew et al., 2023). The model, based on XLM-RoBERTa, computes span representations (concatenated start/end token embeddings and a span width vector), scoring gaps via a dot product with exemplar representations ([CLS] token embedding with explicit gap marking). Probability assignment is modified by a compatibility term $(h_s \cdot h_{exemplar})$, enabling conditioning on exercise type without explicit annotation. Training utilizes the GF2 dataset (French grammar; 768 documents, 5,530 annotated gaps). Binary span prediction achieves an average F1 score of 82%—an 8-point improvement over a baseline classifier. Gap type disentangling (e.g., verb tenses) yields macro F1 scores rising from 13.9% (baseline) to 24.4% (example-aware), indicating effective transfer from exemplars and supporting adaptive, annotation-free gap exercise generation.

8. Gap-Focused Question Generation in Dialogue Assessment

GFQ (Gap-Focused Question) synthesis (Rabin et al., 2023) applies GSG to the dynamic generation of questions targeting information absent from partial answers (e.g., student responses). The pipeline leverages a constituency parser for span extraction, T5-based question generation (with SQuAD fine-tuning), and a question answering model for answerability filtering. Candidate questions are ranked to minimize unknown information beyond the common ground. Human rating averages ascend from 3.72 (unfiltered) to 3.94 (post-ranking), approaching human-written question scores (4.06). Applications span educational dialogues, support-line bots, and automated fact checking, highlighting GSG’s utility in interactive and diagnostic NLP.

9. Cross-Modal Embedding Alignment for Gap Bridging in RAG Systems

GSG in multi-modal retrieval contexts (Yadav et al., 30 Oct 2024) is addressed by a generalized projection method: embeddings from disjoint modalities (e.g., code/pseudocode, bilingual text) are mapped into a unified space via a lightweight projection network (adapter-inspired; three linear layers with interleaved ReLU activations), enabling effective pairing by cosine or Euclidean similarity. Mathematical formulation is $$E_B' = W_3 \cdot \sigma_2 (W_2 \cdot \sigma_1(W_1 \cdot E_B + b_1) + b_2) + b_3$$. Empirical results on English-French sentence retrieval achieve F1=0.9653, outperforming BM25 and DPR, with low latency (0.042s/query) and high throughput (23–24 queries/s). This approach is generalizable (code/pseudocode, bilingual alignment), resource-efficient (training on a single GPU, 5 epochs), and suitable for real-time, resource-constrained GSG in RAG setups—where gap sentences must guide retrieval across semantic discontinuities.

10. Significance and Research Directions

GSG encapsulates a broad set of architectures and techniques applicable across NLG, parsing, summarization, education, dialogue, and cross-modal retrieval. Effectiveness is measured in perplexity, BLEU/ROUGE, F1, and human evaluation scores, with emerging methods leveraging context, neural conditioning, and explicit representation alignment. The generalization of GSG strategies—from template abstraction and genetic exploration to attention-based editing, transformer-based infilling, exemplar-guided selection, context-aware question synthesis, and modality-bridging projections—demonstrates the versatility and depth of current approaches. Ongoing research focuses on tailoring self-supervised objectives, improving grammaticality and semantic control, expanding cross-linguistic and cross-modal coverage, and adapting gap sentence frameworks for new domains and tasks.