Must-See! Complete Collection of NLP Interview Questions (38)

271. Please explain the concept of word vectors (Word Embedding), and describe the working principles of the CBOW (Continuous Bag-of-Words) and Skip-Gram architectures in the Word2Vec model, as well as their advantages and disadvantages in different application scenarios.

• • Analysis: This question mainly tests the understanding of the basic concept of word vectors and the mastery of the important model architecture of Word2Vec. Word vectors are important tools in NLP, used to represent vocabulary as low-dimensional vectors, so that semantically similar words are close together in the vector space. CBOW and Skip-Gram are two implementations of Word2Vec, and understanding their working principles and application scenario differences is crucial.
• • Reference Answer: Word vectors are a representation method that maps vocabulary in natural language to low-dimensional real-valued vector spaces. Through this representation, the semantic and syntactic relationships between words can be reflected through the distances and operations between vectors. For example, “king” and “queen” are semantically close, and their word vectors will be relatively close in space.
• • The CBOW architecture predicts the word vector of the center word using the word vectors of the surrounding context. Its working principle is to average or sum the word vectors of the context words (e.g., n words before and after the target word) as input, then use a neural network (usually a simple multi-layer perceptron) to predict the word vector of the center word. The advantage is that it handles low-frequency words well and has a relatively fast training speed because it can utilize the statistical advantages of context information; the disadvantage is that it does not fully utilize the sequential information of the context words.
• • The Skip-Gram architecture, on the other hand, predicts the word vectors of the context using the center word. Given a center word, its goal is to predict the word vectors of the context words within a certain window range. The advantage is that it can better capture the semantic relationships between words and deeply mine the semantic information of words, especially suitable for tasks requiring precise semantic understanding; the disadvantage is that it has a slower training speed because it needs to predict multiple context words for each center word and may not perform as well as CBOW for low-frequency words. In terms of application scenarios, CBOW is suitable for preliminary semantic analysis of text, such as the preprocessing stage of text classification; Skip-Gram is more suitable for tasks requiring precise semantic representation, such as entity relationship mining in knowledge graphs and high-quality text generation.

272. In text generation tasks, how do you evaluate the quality of generated text? Please detail the calculation methods and characteristics of the three evaluation metrics: BLEU (Bilingual Evaluation Understudy), ROUGE (Recall-Oriented Understudy for Gisting Evaluation), and METEOR (Metric for Evaluation of Translation with Explicit Ordering).

• • Analysis: Evaluating the quality of text generation is an important part of NLP, and these evaluation metrics are important tools for measuring the quality of generated text. Understanding their calculation methods and characteristics helps assess the performance of text generation models.
• • Reference Answer:
• • BLEU Metric: It is mainly used to evaluate the quality of text generation tasks such as machine translation. The calculation method is based on the matching of n-grams (combinations of n consecutive words). For example, BLEU-4 counts the number of matching 4-grams in the generated text and the reference text. The specific steps are to first calculate the overlapping parts of n-grams between the generated text and the reference text, and then integrate the matching situation of different n values (usually n = 1,2,3,4) using geometric mean. The characteristic is that it is simple and intuitive, with a certain correlation to human judgments of translation quality, but it only focuses on precise matching of vocabulary and ignores cases of semantically equivalent vocabulary substitution, and does not adequately consider the fluency and semantic coherence of the generated text.
• • ROUGE Metric: It is mainly used to evaluate tasks such as text summarization and machine translation. It is a recall-based metric that calculates the proportion of the overlapping parts between the generated text and the reference text relative to the reference text. For example, ROUGE-N calculates the overlapping ratio of N-grams between the generated text and the reference text, while ROUGE-L calculates recall based on the longest common subsequence. The characteristic is that it focuses more on recalling information from the reference text, which can reflect the coverage of the generated text to some extent, but it is insufficient in evaluating the grammatical correctness and novelty of the generated text.
• • METEOR Metric: It comprehensively considers factors such as precise matching of vocabulary, stem matching, synonym matching, and the word order of the generated text. The calculation process is relatively complex; it first finds the matching units (which can be words, stems, or synonyms) between the generated text and the reference text, and then calculates the score based on the matching situation and word order. The characteristic is that it has a better correlation with human evaluation results and can more comprehensively assess the quality of generated text, including semantic and grammatical aspects, but the computational cost is high and it is sensitive to parameter settings.

273. Please describe the task and challenges of Named Entity Recognition (NER), and give examples of how to use deep learning models (such as Bi-LSTM-CRF) to tackle these challenges.

• • Analysis: This question tests the understanding of the NER task and how to use deep learning models to address the challenges in the task. NER is an important information extraction task in NLP, with many difficulties, such as ambiguous entity boundaries and diverse entity types.
• • Reference Answer: The task of Named Entity Recognition is to identify entities with specific meanings from text and classify them into predefined categories, such as person names, place names, organization names, dates, etc. For example, in the sentence “Apple Inc. released the iPhone 13 in 2021,” it is necessary to identify “Apple Inc.” as an organization name, “2021” as a time, and “iPhone 13” as a product name.
• • The challenges mainly include the following points: first, entity boundaries are ambiguous; some entities may consist of multiple consecutive words, making it difficult to determine their start and end positions; second, the diversity of entity types; different fields and text types may introduce various new entity types; third, context dependency; the recognition of entities often relies on the surrounding vocabulary and sentence structure.
• • The Bi-LSTM-CRF (Bidirectional Long Short-Term Memory – Conditional Random Field) model can effectively address these challenges. The Bi-LSTM part can handle both forward and backward sequences of text, thereby better capturing context information. For example, for recognizing an entity, it can determine the entity’s boundaries and types using the surrounding context. Long Short-Term Memory (LSTM) can solve the gradient vanishing problem in long sequences, allowing the model to learn long-distance dependencies. The CRF (Conditional Random Field) layer is used to post-process the outputs of the Bi-LSTM; it considers the global optimal solution of the entire label sequence, rather than just the optimal solution of individual labels as in simple classification models. For instance, when predicting entity labels, the CRF adjusts the final prediction results based on the constraints between adjacent labels (such as the “person name” label is more likely to be followed by an “organization name” label), thus improving the accuracy of Named Entity Recognition.

274. In sentiment analysis tasks, how do deep learning models utilize the syntactic structure and semantic information of sentences to improve analysis accuracy? Please illustrate with the Transformer architecture as an example.

• • Analysis: This question focuses on how deep learning models utilize syntactic structure and semantic information in sentiment analysis tasks. The Transformer architecture is an advanced architecture in NLP, making it necessary to understand its application mechanisms in sentiment analysis.
• • Reference Answer: In sentiment analysis tasks, the Transformer architecture effectively utilizes the syntactic structure and semantic information of sentences through the Multi-Head Attention mechanism. Multi-Head Attention can simultaneously focus on different information in different representation subspaces of sentences.
• • In terms of syntactic structure utilization, the Transformer can learn the dependency relationships between words in a sentence. For example, in a sentence, there are grammatical associations between the subject and predicate, and between the predicate and object; the Multi-Head Attention mechanism can capture these relationships. By weighting the attention of words at different positions, the model can understand the syntactic structure of the sentence, such as determining whether the sentence is affirmative or negative, which is very important for sentiment analysis. For example, in the sentence “I do not like this movie,” the position of the word “not” and its relationship with “like” can be learned by the model through the attention mechanism, thus determining the negative sentiment of the sentence.
• • In terms of semantic information utilization, the Transformer uses self-attention mechanisms to semantically encode each word in the sentence. The representation vectors of each word are dynamically adjusted based on the semantics of other words in the sentence. In sentiment analysis, this helps to uncover semantic associations between words. For instance, for sentences that express emotions metaphorically or euphemistically, the model can understand the emotional tendency through semantic encoding. For example, in the sentence “The plot of this movie is a bit dragged out, but the visuals are still beautiful,” the model can determine that the sentence has both negative evaluations (dragged out plot) and positive evaluations (beautiful visuals), leading to a more accurate sentiment analysis result when considered comprehensively. Furthermore, the position encoding in the Transformer architecture also helps to retain the order information of the sentence, enhancing semantic understanding and improving the accuracy of sentiment analysis.

275. Please explain how pre-trained language models (such as BERT, GPT) are applied in downstream tasks of NLP (such as question answering systems, text classification), and what issues need to be noted during the fine-tuning process.

• • Analysis: Pre-trained language models are a popular technology in current NLP, and this question examines their application methods in downstream tasks and key points in fine-tuning. Understanding these contents is crucial for effectively utilizing pre-trained models.
• • Reference Answer: Application methods in downstream tasks:
• • Question Answering Systems: Taking BERT as an example, in question answering systems, the question and the text paragraph containing the answer are usually input together into the BERT model. BERT can jointly encode the question and text, locating the relevant parts of the text paragraph as the answer based on the semantic and syntactic knowledge learned during pre-training. For example, for the question “Who was the first person to walk on the moon?” and a text paragraph containing an introduction to the Apollo 11 mission, BERT can find the answer “Neil Armstrong” through semantic understanding of the text. GPT can generate possible answers based on the question and existing knowledge through a generative approach.
• • Text Classification: For BERT, after inputting the text into the model, the output vector of the [CLS] token (which represents the semantics of the entire sentence) is obtained, and this vector is input into a simple classifier (such as a fully connected layer) for text classification. GPT can also complete tasks by classifying the generated text, but its generative nature gives it unique applications in some classification tasks that require generating explanatory content.
• • Key issues to note during the fine-tuning process:
• • Data Quantity and Quality: If the data quantity for the downstream task is small, it may lead to model overfitting. Attention should be paid to data quality, ensuring accurate and consistent annotations. For example, in text classification fine-tuning, if the annotated data contains many errors, it will severely affect the performance of the fine-tuned model.
• • Hyperparameter Tuning: Hyperparameters such as learning rate during fine-tuning are critical. Since the pre-trained model already has certain parameter settings, it is necessary to choose an appropriate learning rate during fine-tuning to avoid disrupting the knowledge learned by the pre-trained model. Generally, a smaller learning rate is a safer choice.
• • Model Architecture Modification: When modifying the architecture of the pre-trained model in downstream tasks, caution is required. For example, when adding a classification layer on top of BERT, it is essential to ensure that the newly added layer integrates well with the output of the original model and does not introduce too many additional parameters that could lead to overfitting.