This year's annual meeting of the Association for Computational Linguistics (ACL 2019) was bigger than ever. Although the conference received 75% more submissions than last year, the quality of the research papers remained high, and so the acceptance rates are almost the same. It is becoming more and more challenging to keep track of the latest research advances in your area with such an overwhelming number of good research papers coming out. So, for your convenience, we've picked up and summarized several interesting research papers that might have particularly useful applications in a business setting. These are also the papers that got lots of attention from the AI community, and most of these studies have been nominated for or awarded ACL Best Paper Awards.

To build an effective product that relies on machine learning, you need a large volume of high-quality training data. For the solution to correctly understand and mimic humans, it's crucial to have a strategy around collecting and annotating training data that optimizes for quality. Join us to learn about the data you need to build solutions like natural language processing, chatbots, and sentiment analysis, with live Q&A to follow.

Your smart contact center can see how many calls changed from neutral to angry. How many calls changed from angry to happy/neutral. And how many calls changed from neutral to happy. This gives you a new metric to judge agent performance and skill. Recordings where customers are converted from angry to happy can picked out in seconds and used to train agents.

"Learning to choose is hard. Learning to choose well is harder. And learning to choose well in a world of unlimited possibilities is harder still, perhaps too hard." When starting a new NLP sentiment analysis project, it can be quite an overwhelming task to narrow down on a select methodology for a given application. Do we use a rule-based model, or do we train a model on our own data? Should we train a neural network, or will a simple linear model meet our requirements?

This framework has been shown to substantially reduce data needs by leveraging domain-independent dimensions, such as social obligations or feedback, which (as we show) can be transferred between domains. In this paper, we conduct a user study and show that the performance of a multidimensional system, which can be adapted from a source domain, is equivalent to that of a one-dimensional baseline, which can only be trained from scratch. 1 Introduction Data-driven approaches to spoken dialogue systems (SDS) are limited by their reliance on substantial amounts of annotated data in the target domain. This can be addressed by considering transfer learning techniques, e.g.

Natural Language Processing (NLP) is a subfield of machine learning concerned with processing and analyzing natural language data, usually in the form of text or audio. Some common challenges within NLP include speech recognition, text generation, and sentiment analysis, while some high-profile products deploying NLP models include Apple's Siri, Amazon's Alexa, and many of the chatbots one might interact with online. To get started with NLP and introduce some of the core concepts in the field, we're going to build a model that tries to predict the sentiment (positive, neutral, or negative) of tweets relating to US Airlines, using the popular Twitter US Airline Sentiment dataset. Code snippets will be included in this post, but for fully reproducible notebooks and scripts, view all of the notebooks and scripts associated with this project on its Comet project page. Let's start by importing some libraries.

Reinforcement learning (RL) is an effective approach to learn an optimal dialog policy for task-oriented visual dialog systems. A common practice is to apply RL on a neural sequence-to-sequence (seq2seq) framework with the action space being the output vocabulary in the decoder. However, it is difficult to design a reward function that can achieve a balance between learning an effective policy and generating a natural dialog response. This paper proposes a novel framework that alternatively trains a RL policy for image guessing and a supervised seq2seq model to improve dialog generation quality. We evaluate our framework on the GuessWhich task and the framework achieves the state-of-the-art performance in both task completion and dialog quality.

This paper focuses on the task of generating long structured sentences with explicit discourse markers, by proposing a new task Sentence Transfer and a novel model architecture TransSent. Previous works on text generation fused semantic and structure information in one mixed hidden representation. However, the structure was difficult to maintain properly when the generated sentence became longer. In this work, we explicitly separate the modeling process of semantic information and structure information. Intuitively, humans produce long sentences by directly connecting discourses with discourse markers like and, but, etc. We thus define a new task called Sentence Transfer. This task represents a long sentence as (head discourse, discourse marker, tail discourse) and aims at tail discourse generation based on head discourse and discourse marker. Then, by connecting original head discourse and generated tail discourse with a discourse marker, we generate a long structured sentence. We also propose a model architecture called TransSent, which models relations between two discourses by interpreting them as transferring from one discourse to the other in the embedding space. Experiment results show that our model achieves better performance in automatic evaluations, and can generate structured sentences with high quality. The datasets can be accessed by https://github.com/1024er/TransSent dataset.

Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope-- i.e., queries that do not fall into any of the system's supported intents. This poses a new challenge because models cannot assume that every query at inference time belongs to a system-supported intent class. Our dataset also covers 150 intent classes over 10 domains, capturing the breadth that a production task-oriented agent must handle. We evaluate a range of benchmark classifiers on our dataset along with several different out-of-scope identification schemes. We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries. Our dataset and evaluation fill an important gap in the field, offering a way of more rigorously and realistically benchmarking text classification in task-driven dialog systems.

User simulators are essential for training reinforcement learning (RL) based dialog models. However, building a good user simulator that models real user behaviors is challenging. We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly. We present implementations of six user simulators trained with different dialog planning and generation methods. We then calculate a set of automatic metrics to evaluate the quality of these simulators both directly and indirectly. We also ask human users to assess the simulators directly and indirectly by rating the simulated dialogs and interacting with the trained systems. This paper presents a comprehensive evaluation framework for user simulator study and provides a better understanding of the pros and cons of different user simulators, as well as their impacts on the trained systems. 1 1 Introduction Reinforcement Learning has gained more and more attention in dialog system training because it treats the dialog planning as a sequential decision problem and focuses on long-term rewards (Su et al., 2017). However, RL requires interaction with the environment, and obtaining real human users to interact with the system is both time-consuming and labor-intensive. Therefore, building user simulators to interact with the system before deployment to real users becomes an economical choice (Williams et al., 2017; Li et al., 2016). But the performance of the user simulator has a direct impact on the trained RL policy.* Equal contribution. 1 The code and data are released at https://github.