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Found 23 papers, 15 papers with code
A Neural Graph-based Local Coherence Model
Entity grids and entity graphs are two frameworks for modeling local coherence.
Tuning-Free Personalized Alignment via Trial-Error-Explain In-Context Learning
Language models are aligned to the collective voice of many, resulting in generic outputs that do not align with specific users' styles.
Learning When to Retrieve, What to Rewrite, and How to Respond in Conversational QA
In this work, we propose a method for enabling LLMs to decide when to retrieve in RAG settings given a conversational context.
Speechworthy Instruction-tuned Language Models
Current instruction-tuned language models are exclusively trained with textual preference data and thus are often not aligned with the unique requirements of other modalities, such as speech.
FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking
API call generation is the cornerstone of large language models' tool-using ability that provides access to the larger world.
Measuring Retrieval Complexity in Question Answering Systems
Further investigation reveals that RC scores strongly correlate with both QA performance and expert judgment across five of the six studied benchmarks, indicating that RC is an effective measure of question difficulty.
On the Role of Summary Content Units in Text Summarization Evaluation
At the heart of the Pyramid evaluation method for text summarization lie human written summary content units (SCUs).
Generating Summaries with Controllable Readability Levels
Readability refers to how easily a reader can understand a written text.
Learning to Reason over Scene Graphs: A Case Study of Finetuning GPT-2 into a Robot Language Model for Grounded Task Planning
Long-horizon task planning is essential for the development of intelligent assistive and service robots.
Incorporating Relevance Feedback for Information-Seeking Retrieval using Few-Shot Document Re-Ranking
Pairing a lexical retriever with a neural re-ranking model has set state-of-the-art performance on large-scale information retrieval datasets.
UKP-SQuARE v2: Explainability and Adversarial Attacks for Trustworthy QA
In this paper, we introduce SQuARE v2, the new version of SQuARE, to provide an explainability infrastructure for comparing models based on methods such as saliency maps and graph-based explanations.
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code
This problem is especially pertinent in natural language generation which requires ever-improving suites of datasets, metrics, and human evaluation to make definitive claims.
FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications.
UKP-SQUARE: An Online Platform for Question Answering Research
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e. g., extractive, abstractive), require different model architectures (e. g., generative, discriminative), and setups (e. g., with or without retrieval).
Smelting Gold and Silver for Improved Multilingual AMR-to-Text Generation
Recent work on multilingual AMR-to-text generation has exclusively focused on data augmentation strategies that utilize silver AMR.
Structural Adapters in Pretrained Language Models for AMR-to-text Generation
Pretrained language models (PLM) have recently advanced graph-to-text generation, where the input graph is linearized into a sequence and fed into the PLM to obtain its representation.
Ranked #1 on
Data-to-Text Generation
on AMR3.0
Investigating Pretrained Language Models for Graph-to-Text Generation
We show that the PLMs BART and T5 achieve new state-of-the-art results and that task-adaptive pretraining strategies improve their performance even further.
Ranked #1 on
KG-to-Text Generation
on WebNLG (All)
Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs
We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation.
Ranked #5 on
KG-to-Text Generation
on AGENDA
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
Following the major success of neural language models (LMs) such as BERT or GPT-2 on a variety of language understanding tasks, recent work focused on injecting (structured) knowledge from external resources into these models.
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations.
Ranked #1 on
Graph-to-Sequence
on WebNLG
Enhancing AMR-to-Text Generation with Dual Graph Representations
Generating text from graph-based data, such as Abstract Meaning Representation (AMR), is a challenging task due to the inherent difficulty in how to properly encode the structure of a graph with labeled edges.
Ranked #11 on
AMR-to-Text Generation
on LDC2017T10
Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference
While recent progress on abstractive summarization has led to remarkably fluent summaries, factual errors in generated summaries still severely limit their use in practice.
struc2vec: Learning Node Representations from Structural Identity
Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec
Ranked #1 on
Node Classification
on Eximtradedata
