Welcome to the fascinating realm of assessing natural language generation (NLG) systems! In this article, we’ll dive into the challenges and various metrics used to evaluate the effectiveness of NLG systems. Unlike assessing classifier metrics, evaluating NLG systems is considerably more complex. So, let’s embark on this perplexing journey together.
Contents
The Fundamental Challenges
One of the most fundamental challenges in assessing NLG systems is the inherent variability of natural language. There are multiple effective ways to convey the same message, making it difficult to establish a single standard of assessment. The datasets used to train NLG systems often provide only a sample of how something should be said, leaving open questions about what comparisons should be made and how so-called “mistakes” should be assessed.
Moreover, determining what exactly to measure adds another layer of complexity. Should we prioritize fluency, truthfulness, communicative effectiveness, or a combination of these factors? Different metrics may capture some aspects while neglecting others, influencing the goals set for the project.
Perplexity: A Glimpse Into NLG Models
Let’s start by exploring perplexity, a metric deeply intertwined with the structure of NLG models. Perplexity measures the average uncertainty or “surprise” of a sequence given a probability distribution. It quantifies the inverse of the assigned probabilities for a given sequence, taking into account the entire sequence’s length.
However, perplexity has its limitations. It heavily depends on the underlying vocabulary, making it susceptible to manipulations such as mapping all words to a single token, which can artificially reduce perplexity without improving the system’s effectiveness. Additionally, comparisons across datasets and models can be challenging due to varying vocabularies and their inherent notions of perplexity.
N-Gram Based Methods: Word-Error Rate and BLEU Scores
Moving on, let’s explore some n-gram based methods commonly used to assess NLG systems. The word-error rate, a measure of edit distance, compares the predicted sequence to the actual sequence normalized by the length of the latter. However, word-error rate only allows for a single comparison, limiting the evaluation to one reference text. Furthermore, this metric is primarily syntactic, failing to capture semantic nuances.
To address the limitations of word-error rate, BLEU scores came into play. BLEU employs modified n-gram precision, considering multiple human-created reference texts for comparison. It balances modified precision and brevity penalty to evaluate the alignment between predicted and actual sequences. However, BLEU scores have been found to poorly correlate with human scores, indicating potential issues in assessing dialogue systems and even certain NLG tasks in the field of natural language understanding.
Exploring More Metrics: ROUGE, METEOR, and CIDEr
In addition to word-error rate and BLEU scores, other metrics offer alternative perspectives for evaluating NLG systems. ROUGE, a recall-focused variant of BLEU, assesses summarization systems. METEOR goes a step further, incorporating semantic notions by considering not only exact matches but also stem versions and synonyms. CIDEr takes a semantic approach by performing comparisons in vector space through weighted cosine similarity between TF-IDF vectors.
Beyond Traditional Metrics: Communication-Based Evaluation
As we delve deeper into NLG assessment, it becomes crucial to consider metrics that focus on real-world communication goals. Instead of solely comparing input and reference texts, evaluating NLG systems based on their ability to effectively communicate in various contexts can provide valuable insights. An example of this is listener accuracy, which assesses how well a system comprehends and responds to messages in a given communication goal, as demonstrated in the assignment and bake off on color reference.
For a more comprehensive understanding of these issues and an insightful perspective, I recommend reading a paper led by Ben Newman. It originated from a course project and offers valuable insights into evaluating NLG systems from a communication standpoint.
In conclusion, assessing NLG systems presents a myriad of challenges, from the inherent variability of natural language to the choice of metrics. Understanding these complexities and adopting a multifaceted approach to evaluation will drive the advancement of NLG and its applications in the field of natural language understanding.
