Fast-and-Frugal Text-Graph Transformers are Effective Link Predictors

Link prediction models can benefit from incorporating textual descriptions of entities and relations, enabling fully inductive learning and flexibility in dynamic graphs. We address the challenge of also capturing rich structured information about the local neighbourhood of entities and their relations, by introducing a Transformer-based approach that effectively integrates textual descriptions with graph structure, reducing the reliance on resource-intensive text encoders. Our experiments on three challenging datasets show that our Fast-and-Frugal Text-Graph (FnF-TG) Transformers achieve superior performance compared to the previous state-of-the-art methods, while maintaining efficiency and scalability.

Enhancing Biomedical Knowledge Discovery for Diseases: An End-To-End Open-Source Framework

The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework designed to construct knowledge around specific diseases directly from raw text. To facilitate research in disease-related knowledge discovery, we create two annotated datasets focused on Rett syndrome and Alzheimer's disease, enabling the identification of semantic relations between biomedical entities. Extensive benchmarking explores various ways to represent relations and entity representations, offering insights into optimal modeling strategies for semantic relation detection and highlighting language models' competence in knowledge discovery. We also conduct probing experiments using different layer representations and attention scores to explore transformers' ability to capture semantic relations.

Contrastive learning of T cell receptor representations

Computational prediction of the interaction of T cell receptors (TCRs) and their ligands is a grand challenge in immunology. Despite advances in high-throughput assays, specificity-labelled TCR data remains sparse. In other domains, the pre-training of language models on unlabelled data has been successfully used to address data bottlenecks. However, it is unclear how to best pre-train protein language models for TCR specificity prediction. Here we introduce a TCR language model called SCEPTR (Simple Contrastive Embedding of the Primary sequence of T cell Receptors), capable of data-efficient transfer learning. Through our model, we introduce a novel pre-training strategy combining autocontrastive learning and masked-language modelling, which enables SCEPTR to achieve its state-of-the-art performance. In contrast, existing protein language models and a variant of SCEPTR pre-trained without autocontrastive learning are outperformed by sequence alignment-based methods. We anticipate that contrastive learning will be a useful paradigm to decode the rules of TCR specificity.

Nonparametric Variational Regularisation of Pretrained Transformers

The current paradigm of large-scale pre-training and fine-tuning Transformer large language models has lead to significant improvements across the board in natural language processing. However, such large models are susceptible to overfitting to their training data, and as a result the models perform poorly when the domain changes. Also, due to the model's scale, the cost of fine-tuning the model to the new domain is large. Nonparametric Variational Information Bottleneck (NVIB) has been proposed as a regulariser for training cross-attention in Transformers, potentially addressing the overfitting problem. We extend the NVIB framework to replace all types of attention functions in Transformers, and show that existing pretrained Transformers can be reinterpreted as Nonparametric Variational (NV) models using a proposed identity initialisation. We then show that changing the initialisation introduces a novel, information-theoretic post-training regularisation in the attention mechanism, which improves out-of-domain generalisation without any training. This success supports the hypothesis that pretrained Transformers are implicitly NV Bayesian models.

Transformers as Graph-to-Graph Models

We argue that Transformers are essentially graph-to-graph models, with sequences just being a special case. Attention weights are functionally equivalent to graph edges. Our Graph-to-Graph Transformer architecture makes this ability explicit, by inputting graph edges into the attention weight computations and predicting graph edges with attention-like functions, thereby integrating explicit graphs into the latent graphs learned by pretrained Transformers. Adding iterative graph refinement provides a joint embedding of input, output, and latent graphs, allowing non-autoregressive graph prediction to optimise the complete graph without any bespoke pipeline or decoding strategy. Empirical results show that this architecture achieves state-of-the-art accuracies for modelling a variety of linguistic structures, integrating very effectively with the latent linguistic representations learned by pretraining.

Learning to Abstract with Nonparametric Variational Information Bottleneck

Learned representations at the level of characters, sub-words, words and sentences, have each contributed to advances in understanding different NLP tasks and linguistic phenomena. However, learning textual embeddings is costly as they are tokenization specific and require different models to be trained for each level of abstraction. We introduce a novel language representation model which can learn to compress to different levels of abstraction at different layers of the same model. We apply Nonparametric Variational Information Bottleneck (NVIB) to stacked Transformer self-attention layers in the encoder, which encourages an information-theoretic compression of the representations through the model. We find that the layers within the model correspond to increasing levels of abstraction and that their representations are more linguistically informed. Finally, we show that NVIB compression results in a model which is more robust to adversarial perturbations.

GADePo: Graph-Assisted Declarative Pooling Transformers for Document-Level Relation Extraction

Document-level relation extraction aims to identify relationships between entities within a document. Current methods rely on text-based encoders and employ various hand-coded pooling heuristics to aggregate information from entity mentions and associated contexts. In this paper, we replace these rigid pooling functions with explicit graph relations by leveraging the intrinsic graph processing capabilities of the Transformer model. We propose a joint text-graph Transformer model, and a graph-assisted declarative pooling (GADePo) specification of the input which provides explicit and high-level instructions for information aggregation. This allows the pooling process to be guided by domain-specific knowledge or desired outcomes but still learned by the Transformer, leading to more flexible and customizable pooling strategies. We extensively evaluate our method across diverse datasets and models, and show that our approach yields promising results that are comparable to those achieved by the hand-coded pooling functions.

TESS: Text-to-Text Self-Conditioned Simplex Diffusion

Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various domains with continuous-valued inputs. Despite the promises of fully non-autoregressive text generation, applying diffusion models to natural language remains challenging due to its discrete nature. In this work, we propose Text-to-text Self-conditioned Simplex Diffusion (TESS), a text diffusion model that is fully non-autoregressive, employs a new form of self-conditioning, and applies the diffusion process on the logit simplex space rather than the typical learned embedding space. Through extensive experiments on natural language understanding and generation tasks including summarization, text simplification, paraphrase generation, and question generation, we demonstrate that TESS outperforms state-of-the-art non-autoregressive models and is competitive with pretrained autoregressive sequence-to-sequence models.

RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question

Existing metrics for evaluating the quality of automatically generated questions such as BLEU, ROUGE, BERTScore, and BLEURT compare the reference and predicted questions, providing a high score when there is a considerable lexical overlap or semantic similarity between the candidate and the reference questions. This approach has two major shortcomings. First, we need expensive human-provided reference questions. Second, it penalises valid questions that may not have high lexical or semantic similarity to the reference questions. In this paper, we propose a new metric, RQUGE, based on the answerability of the candidate question given the context. The metric consists of a question-answering and a span scorer module, in which we use pre-trained models from the existing literature, and therefore, our metric can be used without further training. We show that RQUGE has a higher correlation with human judgment without relying on the reference question. RQUGE is shown to be significantly more robust to several adversarial corruptions. Additionally, we illustrate that we can significantly improve the performance of QA models on out-of-domain datasets by fine-tuning on the synthetic data generated by a question generation model and re-ranked by RQUGE.

A Variational AutoEncoder for Transformers with Nonparametric Variational Information Bottleneck

We propose a VAE for Transformers by developing a variational information bottleneck regulariser for Transformer embeddings. We formalise the embedding space of Transformer encoders as mixture probability distributions, and use Bayesian nonparametrics to derive a nonparametric variational information bottleneck (NVIB) for such attention-based embeddings. The variable number of mixture components supported by nonparametric methods captures the variable number of vectors supported by attention, and the exchangeability of our nonparametric distributions captures the permutation invariance of attention. This allows NVIB to regularise the number of vectors accessible with attention, as well as the amount of information in individual vectors. By regularising the cross-attention of a Transformer encoder-decoder with NVIB, we propose a nonparametric variational autoencoder (NVAE). Initial experiments on training a NVAE on natural language text show that the induced embedding space has the desired properties of a VAE for Transformers.