In this paper, we design headword-oriented entity linking (HEL), a specialized entity linking problem in which only the headwords of the entities are to be linked to knowledge bases; mention scopes of the entities do not need to be identified in the problem setting. This special task is motivated by the fact that in many articles referring to specific products, the complete full product names are rarely written; instead, they are often abbreviated to shorter, irregular versions or even just to their headwords, which are usually their product types, such as “stick” or “mask” in a cosmetic context. To fully design the special task, we construct a labeled cosmetic corpus as a public benchmark for this problem, and propose a product embedding model to address the task, where each product corresponds to a dense representation to encode the different information on products and their context jointly. Besides, to increase training data, we propose a special transfer learning framework in which distant supervision with heuristic patterns is first utilized, followed by supervised learning using a small amount of manually labeled data. The experimental results show that our model provides a strong benchmark performance on the special task.

Embedding commonsense knowledge is crucial for end-to-end models to generalize inference beyond training corpora. However, existing word analogy datasets have tended to be handcrafted, involving permutations of hundreds of words with only dozens of pre-defined relations, mostly morphological relations and named entities. In this work, we model commonsense knowledge down to word-level analogical reasoning by leveraging E-HowNet, an ontology that annotates 88K Chinese words with their structured sense definitions and English translations. We present CA-EHN, the first commonsense word analogy dataset containing 90,505 analogies covering 5,656 words and 763 relations. Experiments show that CA-EHN stands out as a great indicator of how well word representations embed commonsense knowledge. The dataset is publicly available at https://github.com/ckiplab/CA-EHN.

Neural encoder-decoder models have shown promising performance for human-computer dialogue systems over the past few years. However, due to the maximum-likelihood objective for the decoder, the generated responses are often universal and safe to the point that they lack meaningful information and are no longer relevant to the post. To address this, in this paper, we propose semantic guidance using reinforcement learning to ensure that the generated responses indeed include the given or predicted semantics and that these semantics do not appear repeatedly in the response. Synsets, which comprise sets of manually defined synonyms, are used as the form of assigned semantics. For a given/assigned/predicted synset, only one of its synonyms should appear in the generated response; this constitutes a simple but effective semantic-control mechanism. We conduct both quantitative and qualitative evaluations, which show that the generated responses are not only higher-quality but also reflect the assigned semantic controls.