Everyday AI

人工智慧,形影相隨

MediaTek

MediaTek is the world’s 4th largest global fabless semiconductor company. We are a market leader in developing innovative systems-on-chip (SoC) for mobile devices, home entertainment, connectivity and IoT products. Ultimately, we power more than 2 billion devices a year – that’s in 20 percent of homes and nearly 1 of every 3 mobile phones globally.

MediaTek Research, the AI research arm of MediaTek, brings the most advanced developments in AI to everyday devices. In addition to designing embedded AI systems, MediaTek Research contributes to the fundamental research of AI-based algorithmic solutions.

Research

Papers

SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues

Dialogue systems are usually categorized into two types, open-domain and task-oriented. The first one focuses on chatting with users and making them engage in the conversations, where selecting a proper topic to fit the dialogue context is essential for a successful dialogue. The other one focuses on a specific task instead of casual talks, e.g., finding a movie on Friday night, playing a song. These two directions have been studied separately due to their different purposes. However, how to smoothly transition from social chatting to task-oriented dialogues is important for triggering the business opportunities, and there is no any public data focusing on such scenarios. Hence, this paper focuses on investigating the conversations starting from open-domain social chatting and then gradually transitioning to task-oriented purposes, and releases a large-scale dataset with detailed annotations for encouraging this research direction. To achieve this goal, this paper proposes a framework to automatically generate many dialogues without human involvement, in which any powerful open-domain dialogue generation model can be easily leveraged. The human evaluation shows that our generated dialogue data has a natural flow at a reasonable quality, showing that our released data has a great potential of guiding future research directions and commercial activities. Furthermore, the released models allow researchers to automatically generate unlimited dialogues in the target scenarios, which can greatly benefit semi-supervised and unsupervised approaches.

SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues

Dialogue systems are usually categorized into two types, open-domain and task-oriented. The first one focuses on chatting with users and making them engage in the conversations, where selecting a proper topic to fit the dialogue context is essential for a successful dialogue. The other one focuses on a specific task instead of casual talks, e.g., finding a movie on Friday night, playing a song. These two directions have been studied separately due to their different purposes. However, how to smoothly transition from social chatting to task-oriented dialogues is important for triggering the business opportunities, and there is no any public data focusing on such scenarios. Hence, this paper focuses on investigating the conversations starting from open-domain social chatting and then gradually transitioning to task-oriented purposes, and releases a large-scale dataset with detailed annotations for encouraging this research direction. To achieve this goal, this paper proposes a framework to automatically generate many dialogues without human involvement, in which any powerful open-domain dialogue generation model can be easily leveraged. The human evaluation shows that our generated dialogue data has a natural flow at a reasonable quality, showing that our released data has a great potential of guiding future research directions and commercial activities. Furthermore, the released models allow researchers to automatically generate unlimited dialogues in the target scenarios, which can greatly benefit semi-supervised and unsupervised approaches.

Talks

Blog

Non-reversible Gaussian Processes

In Machine Learning, it is often convenient to use functions that are not fixed but only known with some probability. For example, let say we know the weather in Cambridge for the last few years and we would like to predict what will be the weather tomorrow (e.g. the temperature or wind speed, a function of time $f(t)$). Or, we know today’s weather in Cambridge and London, and we would like to know what is happening along the M11 motorway between the two cities. Instead of giving one predicted value, we would like a probability distribution for each time (or space point). Since time is continuous, we are looking for a distribution over functions $f(t)$. Gaussian Processes (GP) represent a simple and powerful example of distribution over functions. They are widely used for modeling and prediction (Rasmussen and Williams 2006), a few examples of GPs are shown in Figure 1. Perhaps the most important feature of a GP is the correlation function $k(t,t’)$, which determines the information we get about $f(t’)$ when measuring the true value of $f(t)$. The shape of this correlation function is the most important choice to make when we use a GP, it determines what kind…

TreeMAML

Many NLP models rely on training in one high-resource language, and they cannot be directly used to make predictions  for other languages at inference time. Most of the languages of the world are under-resourced and rely on Machine Translation (MT) to English to make use of Language Models. However, having an MT system in every direction is costly and  not the best solution for every NLP task. We propose the use of meta-learning to solve this issue. Our algorithm, TreeMAML, extends a meta-learning model, MAML [1], by exploiting hierarchical languages relationships. MAML adapts the model to each task with a few gradient steps. In our method, TreeMAML, this adaptation follows the hierarchical tree structure:In each step down the tree, gradients are pooled across language clusters: Algorithm 1.Algorithm 2 is a non-binary modification of the OTD clustering [2],  that generates the language tree without previous knowledge of the structure, allowing us to use implicit relationships between the languages.In our Experiments we adapt a high-resource language model, Multi-BERT [3], to a Few-Shot NLI task with these steps:We use the XNLI data set [4]. XNLI corpus is a crowd-sourced collection of pairs for the MultiNLI corpus with 10 different genres in 15 languages. The pairs are…

Non-reversible Gaussian Processes

In Machine Learning, it is often convenient to use functions that are not fixed but only known with some probability. For example, let say we…

TreeMAML

Many NLP models rely on training in one high-resource language, and they cannot be directly used to make predictions  for other languages at inference time. Most…

About Us

We at MediaTek Research are a dedicated research unit of the global MediaTek Group. Our mission is to explore and initiate emerging approaches in AI and ML from both fundamental and applied perspectives. We have two research centers, one in Cambridge (UK), and one in National Taiwan University. We cooperate closely with worldwide academic community.

Our team is made of researchers with a diverse background in computer science, engineering, mathematics and physics.

Contact Us

Interested in knowing more about MediaTek Research? Please feel free to contact us:

 

Our Email Address: info@mtkresearch.com