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Found 15 papers, 4 papers with code
Adaptive Bridge between Training and Inference for Dialogue Generation
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario. In real human dialogue, there are many appropriate responses for the same context, not only with different expressions, but also with different topics.
Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation
As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms. The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase.
Summarizing Dialogues with Negative Cues
In this work, rather than directly forcing a summarization system to merely pay more attention to the salient pieces, we propose to explicitly have the model perceive the redundant parts of an input dialogue history during the training phase.
Automatic Scene-based Topic Channel Construction System for E-Commerce
To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.
Automatic Product Copywriting for E-Commerce
It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.
Adaptive Bridge between Training and Inference for Dialogue
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scenario.
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning
Furthermore, the consistency signals between each candidate and the speaker's own history are considered to drive a model to prefer a candidate that is logically consistent with the speaker's history logic.
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
To capture the various topic information of a conversation and outline salient facts for the captured topics, this work proposes two topic-aware contrastive learning objectives, namely coherence detection and sub-summary generation objectives, which are expected to implicitly model the topic change and handle information scattering challenges for the dialogue summarization task.
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Text Summarization
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Pre-training for Abstractive Document Summarization by Reinstating Source Text
The main idea is that, given an input text artificially constructed from a document, a model is pre-trained to reinstate the original document.
Mining Commonsense Facts from the Physical World
In this paper, we propose a new task of mining commonsense facts from the raw text that describes the physical world.
Aligning Cross-Lingual Entities with Multi-Aspect Information
Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages.
Text2Math: End-to-end Parsing Text into Math Expressions
We propose Text2Math, a model for semantically parsing text into math expressions.
Joint Detection and Location of English Puns
A pun is a form of wordplay for an intended humorous or rhetorical effect, where a word suggests two or more meanings by exploiting polysemy (homographic pun) or phonological similarity to another word (heterographic pun).
Quantity Tagger: A Latent-Variable Sequence Labeling Approach to Solving Addition-Subtraction Word Problems
An arithmetic word problem typically includes a textual description containing several constant quantities.
Learning Cross-lingual Distributed Logical Representations for Semantic Parsing
In this work, we present a study to show how learning distributed representations of the logical forms from data annotated in different languages can be used for improving the performance of a monolingual semantic parser.
