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1. Learning to Scale Multilingual Representations for Vision-Language Tasks

   Andrea Burns, Donghyun Kim (January 2020, European Conference on Computer Vision)

   null (Ed.)

   Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we-9*6 propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model parameters without sacrificing downstream task performance. SMALR learns a fixed size language-agnostic representation for most words in a multilingual vocabulary, keeping language-specific features for just a few. We use a masked cross-language modeling loss to align features with context from other languages. Additionally, we propose a cross-lingual consistency module that ensures predictions made for a query and its machine translation are comparable. The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3–4% with less than 1/5th the training parameters compared to other word embedding methods. 

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2. Learning to Scale Multilingual Representations for Vision-Language Tasks

   https://doi.org/10.1007/978-3-030-58548-8  

   Andrea Burns, Donghyun Kim (January 2020, European Conference on Computer Vision)

   null (Ed.)

   Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added. In this paper, we-9*6 propose a Scalable Multilingual Aligned Language Representation (SMALR) that supports many languages with few model parameters without sacrificing downstream task performance. SMALR learns a fixed size language-agnostic representation for most words in a multilingual vocabulary, keeping language-specific features for just a few. We use a masked cross-language modeling loss to align features with context from other languages. Additionally, we propose a cross-lingual consistency module that ensures predictions made for a query and its machine translation are comparable. The effectiveness of SMALR is demonstrated with ten diverse languages, over twice the number supported in vision-language tasks to date. We evaluate on multilingual image-sentence retrieval and outperform prior work by 3–4% with less than 1/5th the training parameters compared to other word embedding methods. 

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3. Autonomous Learning of Object-Centric Abstractions for High-Level Planning

   James, S; Rosman, B; Konidaris, G.D. (April 2022, Proceedings of the The Tenth International Conference on Learning Representations)

   We propose a method for autonomously learning an object-centric representation of a continuous and high-dimensional environment that is suitable for planning. Such representations can immediately be transferred between tasks that share the same types of objects, resulting in agents that require fewer samples to learn a model of a new task. We first demonstrate our approach on a 2D crafting domain consisting of numerous objects where the agent learns a compact, lifted representation that generalises across objects. We then apply it to a series of Minecraft tasks to learn object-centric representations and object types---directly from pixel data---that can be leveraged to solve new tasks quickly. The resulting learned representations enable the use of a task-level planner, resulting in an agent capable of transferring learned representations to form complex, long-term plans. 

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4. Explanation as Question Answering based on a Task Model of the Agent’s Design

   Ashok Goel, Harshvardhan Sikka (July 2022, Procs. IJCAI-2022 Workshop on Explainable AI)

   We describe a human-centered and design-based stance towards generating explanations in AI agents. We collect questions about the working of an AI agent through participatory design by fo- cus groups. We capture an agent’s design through a Task-Method-Knowledge model that explicitly specifies the agent’s tasks and goals, as well as the mechanisms, knowledge and vocabulary it uses for accomplishing the tasks. We illustrate our approach through the generation of explanations in Skillsync, an AI agent that links companies and colleges for worker upskilling and reskilling. In particular, we embed a question-answering agent called AskJill in Skillsync, where AskJill contains a TMK model of Skillsync’s design. AskJill presently answers human-generated questions about Skillsync’s tasks and vocabulary, and thereby helps explain how it produces its recommendations. 

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5. Ember: no-code context enrichment via similarity-based keyless joins

   https://doi.org/10.14778/3494124.3494149  

   Suri, Sahaana; Ilyas, Ihab F.; Ré, Christopher; Rekatsinas, Theodoros (November 2021, Proceedings of the VLDB Endowment)

   Structured data, or data that adheres to a pre-defined schema, can suffer from fragmented context: information describing a single entity can be scattered across multiple datasets or tables tailored for specific business needs, with no explicit linking keys. Context enrichment, or rebuilding fragmented context, using keyless joins is an implicit or explicit step in machine learning (ML) pipelines over structured data sources. This process is tedious, domain-specific, and lacks support in now-prevalent no-code ML systems that let users create ML pipelines using just input data and high-level configuration files. In response, we propose Ember, a system that abstracts and automates keyless joins to generalize context enrichment. Our key insight is that Ember can enable a general keyless join operator by constructing an index populated with task-specific embeddings. Ember learns these embeddings by leveraging Transformer-based representation learning techniques. We describe our architectural principles and operators when developing Ember, and empirically demonstrate that Ember allows users to develop no-code context enrichment pipelines for five domains, including search, recommendation and question answering, and can exceed alternatives by up to 39% recall, with as little as a single line configuration change. 

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