Semantics-aware BERT for Language Understanding

The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks. However, the existing language representation models including ELMo, GPT and BERT only exploit plain context-sensitive features such as character or word embeddings. They rarely consider incorporating structured semantic information which can provide rich semantics for language representation. To promote natural language understanding, we propose to incorporate explicit contextual semantics from pre-trained semantic role labeling, and introduce an improved language representation model, Semantics-aware BERT (SemBERT), which is capable of explicitly absorbing contextual semantics over a BERT backbone. SemBERT keeps the convenient usability of its BERT precursor in a light fine-tuning way without substantial task-specific modifications. Compared with BERT, semantics-aware BERT is as simple in concept but more powerful. It obtains new state-of-the-art or substantially improves results on ten reading comprehension and language inference tasks.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020

TDLR: Top (\u3cem\u3eSemantic\u3c/em\u3e)-Down (\u3cem\u3eSyntactic\u3c/em\u3e) Language Representation

Language understanding involves processing text with both the grammatical and common-sense contexts of the text fragments. The text “I went to the grocery store and brought home a car” requires both the grammatical context (syntactic) and common-sense context (semantic) to capture the oddity in the sentence. Contextualized text representations learned by Language Models (LMs) are expected to capture a variety of syntactic and semantic contexts from large amounts of training data corpora. Recent work such as ERNIE has shown that infusing the knowledge contexts, where they are available in LMs, results in significant performance gains on General Language Understanding (GLUE) benchmark tasks. However, to our knowledge, no knowledge-aware model has attempted to infuse knowledge through top-down semantics-driven syntactic processing (Eg: Common-sense to Grammatical) and directly operated on the attention mechanism that LMs leverage to learn the data context. We propose a learning framework Top-Down Language Representation (TDLR) to infuse common-sense semantics into LMs. In our implementation, we build on BERT for its rich syntactic knowledge and use the knowledge graphs ConceptNet and WordNet to infuse semantic knowledge

Externalism, metasemantic contextualism, and self-knowledge

This paper examines some of the interactions between holism, contextualism, and externalism, and will argue that an externalist metasemantics that grounds itself in certain plausible assumptions about self- knowledge will also be a contextualist metasemantics, and that such a contextualist metasemantics in turn resolves one of the best known problems externalist theories purportedly have with self-knowledge, namely the problem of how the possibility of various sorts of ‘switching’ cases can appear to undermine the ‘transparency’ of our thoughts (in particular, our ability to tell, with respect to any two occurrent thoughts, whether they exercise the same or different concepts)

Clue: Cross-modal Coherence Modeling for Caption Generation

We use coherence relations inspired by computational models of discourse to study the information needs and goals of image captioning. Using an annotation protocol specifically devised for capturing image--caption coherence relations, we annotate 10,000 instances from publicly-available image--caption pairs. We introduce a new task for learning inferences in imagery and text, coherence relation prediction, and show that these coherence annotations can be exploited to learn relation classifiers as an intermediary step, and also train coherence-aware, controllable image captioning models. The results show a dramatic improvement in the consistency and quality of the generated captions with respect to information needs specified via coherence relations.Comment: Accepted as a long paper to ACL 202