Knowledge-Augmented-Language-Models

Must-read papers of Knowledge-Augmented Language Models and papers relevant to knowledge injection.

Knowledge-Augmented Language Models

• K-BERT: Enabling Language Representation with Knowledge Graph [pdf]
  • Inject triplets from KGs by adding them to relevant entities, which leads to inputs of tree-structure;
  • To obtain the tree-structure in Transformer, they propose to modify the position embedding and the attention mask matrix, keeping the injected knowledge invisible to irrelevant tokens;
  • Challenges addressed: (1) heterogeneous embedding space and (2) knowledge noise;
  • Pretraining tasks: MLM (Masked Language Modeling) and NSP (Next Sentence Prediction).
• Knowledge Enhanced Contextual Word Representations [pdf]
  • Introduce a Knowledge Attention and Recontextualization (KAR) component to integrate KB knowledge into PLMs (e.g., BERT);
  • The KAR layer can be inserted between two Transformer layers and it calculates attention scores over entity-span representations, which will be used to recontextualize output embeddings of a Transformer block;
  • Entity-span representations = contextualized mention-span representations + weighted entity embeddings (from a KB)
  • Pretraining tasks: MLM, NSP, and EL (Entity Linking)
• ERNIE: Enhanced Language Representation with Informative Entities [pdf]
  • M Knowledgeable Encoders (K-Encoder) are appended after N Transformer Encoders;
  • In a K-Encoder, retrieved entity embeddings are first contextualized, then aggregated with their aligned token embeddings, and then all the embeddings (tokens & entities) are updated;
  • Pretraining tasks: MLM, NSP, and dEA (denoising Entity Autoencoder)
  • dEA: (1) In 5% of the time, for a given token-entity alignment, replace the entity with another random entity, (2) in 15% of the time, mask token-entity alignments, and (3) in the rest of the time, keep token-entity alignments unchanged.
• KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning [pdf]
  • M KG-augmented Encoders are appended to N BART Encoders; M KG-augmented Decoders are appended to N BART Decoders;
  • Inject knowledge from KGs by using Multi-head Graph Attention over the input subgraphs for both KG-augmented encoders and decoders;
  • Use subgraphs of different granularity in KG-augmented decoders (w/ one-hop neighborring nodes) compared with encoders;
  • Pretraining tasks: generate the original concept token from the masked concept nodes
• K-ADAPTER: Infusing Knowledge into Pre-Trained Models with Adapters [pdf]
  • Parameters of the model are fixed, and two adapters are inserted into the LM, which will be separately pre-trained on the tasks;
  • Pretraining tasks: predication prediction (classify relation labels of given entity pairs based on context), and dependency relation prediction (predict the head index of each token in the given sentence).
• DKPLM: Decomposable Knowledge-Enhanced Pre-trained Language Model for Natural Language Understanding, in AAAI 2022. [pdf]
  • Pretraining tasks: MLM and De (relational knowledge decoding);
  • De: (1) Knowledge-aware long-tail entity detection (2) calculate (long-tail) head/tail entity embeddings (3) calculate the loss against a sampled entity list with Sampled Softmax Function.
• Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph, in EMNLP 2020. [pdf]
  • Reason on a KG, model the distribution of nodes in the KG, and learn to directly copy concepts (node) from the KG during generation;
  • The distribution of nodes is obtained by calculating scores of each node (node score). The score of node consists of two parts: (1) node scores from previous hops, and (2) triple relevance that reflects the relevancy of the evidence given by the triplet (u, r, v) under the current context (LM output embeddings);
  • Pretraining tasks: (1) language modeling, (2) a gate which controls copying concepts from the KG, and (3) weak supervision of matching triplets along "gold reasoning paths" gathered by breadth-first search.
• KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation, in TACL 2020. [pdf]
  • Pretrain tasks: MLM and KE (Knowledge Embedding);
  • KE: (1) maintain a set of relation embeddings, (2) use a shared encoder to encode texts of h, r, and t (w/ or w/o descriptions) to get the corresponding embeddings, and then (3) calculate the KE loss with negative sampling.
• Relation-Guided Pre-Training for Open-Domain Question Answering [pdf]
  • Construct a Grounded Relational Wiki-Graph, where each relation triplet 〈s, r, t〉 is linked to a set of description passages {desc.(s, t)} in the Wiki-page of entity s;
  • Generate relational QA pair for relation-guided QA pretraining (based on the DPR framework);
  • Pretraining tasks (same as DPR): (1) Retriever (dense retrieval); (2) Reader (rerank & span extraction)
• Facts as Experts: Adaptable and Interpretable Neural Memory over Symbolic Knowledge [pdf]
  • Add a KB of facts on the basis of EaE, where for a single 〈s, r, t〉, the concat of s and r serves as the key while t is the value;
  • Tail entitys t_i in the Fact Memory share the same weights with embeddings in the Entity Memory;
  • In addition to EaE, FaE also learns to retrieve facts given the context, and learns to answer the masked position (only one masked mention is picked) considering both contextual information and fact knowledge;
  • Pretraining tasks: entity linking (at the memory layer), entity linking (at the last Transformer block), fact retrieval, masked mention modeling.
• Entities as Experts: Sparse Memory Access with Entity Supervision [pdf]
  • Insert an Entity Memory Layer between two sets of Transformer blocks, which retrieves entity embeddings that are related to mentions in the input sentence;
  • Weighted sum of the entity embeddings will be added to the previous Transformer output embeddings;
  • Entity embeddings will also be updated using the mentions (pseudo entity embeddings);
  • Pretraining tasks: MLM, mention boundary detection, entity linking.

• JAKET: Joint Pre-training of Knowledge Graph and Language Understanding [pdf]
  • Model design: there are two modules, i.e., a Knowledge Module (KM) containing a GAT and a Language Module (LM) containing a language model, which will mutually benefits each other;
  • They propose a decomposed LM (LM1 + LM2) to solve cyclic dependency between KM and LM;
  • Memory design: entity embeddings are initialized using LM1, and the whole memory will be updated with an increasing interval;
  • Pretraining tasks: entity category prediction and relation type prediction (Knowledge Module); MLM, and masked entity prediction (Language Module).
• JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs [pdf]
  • KR: linearized KG;
  • Model design: a Encoder-Decoder model, where input is the concatenation of a linearized graph and a sequence of texts, output is a sequence of texts;
  • Pretrain tasks: graph enhanced text reconstruction, text enhanced graph reconstruction, graph-text embedding alignment.
• CoLAKE: Contextualized Language and Knowledge Embedding [pdf]
  • Construct word-knowledge graphs by adding relations and entities to the anchor nodes;
  • Graphs are linearized and input into the LM, and the structural information is maintained by modifying self-attention masks;
  • Pretraining tasks (Graph-text alignment): masking word nodes (MLM), masking entity nodes, masking relation nodes.
• Empowering Language Models with Knowledge Graph Reasoning for Question Answering [pdf]
  • Model design: T Knowledge-Injection Layers (KIL) are inserted into an LM to achieve at most T-hop reasoning on KGs;
  • Factorize the process of answering a question into two parts: (1) KG reasoning, and (2) a knowledge-injected LM;
  • KG reasoning includes relation prediction and entity distribution update using a Contextualized Random Walk;
  • Pretraining tasks: entity linking loss, weakly supervised relation path loss.

Knowledge-Augmented Vision-Language Models

Knowledge Injection for Specific Tasks

• KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
• Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
• QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering
• GREASELM: Graph Reasoning Enhanced Language Models for Question Answering (Multi-choice question answering)
  • Incorporate M GreaseLM Layers after N LM-layers (encoders);
  • A GreaseLM Layer constitutes of an LM layer and a GNN layer;
  • In each layer, a specific token called interaction node is used to interact with another modal;
  • Final embeddings of the interaction nodes from both modals and the pooled GNN embedding will be used for downstream tasks.

Checklist

• Papers of Knowledge-Augmented Language Models
• Papers of Knowledge-Augmented Vision-Language Models